From 2ce33b2bd60079bd602c1764f835fadf49e6e9b8 Mon Sep 17 00:00:00 2001 From: rchiodo <19672699+rchiodo@users.noreply.github.com> Date: Tue, 29 Sep 2026 06:10:23 +0000 Subject: [PATCH] Update benchmark results from workflow runner --- latest-results.md | 324 +- ...lsp-mypy-20260929T060553Z-responses.jsonl} | 0 ....json => pylsp-mypy-20260929T060553Z.json} | 2754 +++---- ... pyrefly-20260929T060553Z-responses.jsonl} | 150 +- ...30Z.json => pyrefly-20260929T060553Z.json} | 6700 +++++++++-------- ... pyright-20260929T060553Z-responses.jsonl} | 0 ...30Z.json => pyright-20260929T060553Z.json} | 2740 +++---- latest-results/summary-20260928T060730Z.csv | 132 - latest-results/summary-20260929T060553Z.csv | 132 + ...30Z.json => summary-20260929T060553Z.json} | 12 +- ...060730Z.md => summary-20260929T060553Z.md} | 324 +- ...nl => ty-20260929T060553Z-responses.jsonl} | 10 +- ...T060730Z.json => ty-20260929T060553Z.json} | 2764 +++---- 13 files changed, 8028 insertions(+), 8014 deletions(-) rename latest-results/{pylsp-mypy-20260928T060730Z-responses.jsonl => pylsp-mypy-20260929T060553Z-responses.jsonl} (100%) rename latest-results/{pylsp-mypy-20260928T060730Z.json => pylsp-mypy-20260929T060553Z.json} (89%) rename latest-results/{pyrefly-20260928T060730Z-responses.jsonl => pyrefly-20260929T060553Z-responses.jsonl} (99%) rename latest-results/{pyrefly-20260928T060730Z.json => pyrefly-20260929T060553Z.json} (91%) rename latest-results/{pyright-20260928T060730Z-responses.jsonl => pyright-20260929T060553Z-responses.jsonl} (100%) rename latest-results/{pyright-20260928T060730Z.json => pyright-20260929T060553Z.json} (90%) delete mode 100644 latest-results/summary-20260928T060730Z.csv create mode 100644 latest-results/summary-20260929T060553Z.csv rename latest-results/{summary-20260928T060730Z.json => summary-20260929T060553Z.json} (93%) rename latest-results/{summary-20260928T060730Z.md => summary-20260929T060553Z.md} (60%) rename latest-results/{ty-20260928T060730Z-responses.jsonl => ty-20260929T060553Z-responses.jsonl} (99%) rename latest-results/{ty-20260928T060730Z.json => ty-20260929T060553Z.json} (89%) diff --git a/latest-results.md b/latest-results.md index c1e2b7d..dfff840 100644 --- a/latest-results.md +++ b/latest-results.md @@ -1,8 +1,8 @@ # Python LSP Benchmark Comparison -Generated from `results/bench-servers/summary-20260928T060730Z.json` +Generated from `results/bench-servers/summary-20260929T060553Z.json` -- Generated at: 20260928T060730Z +- Generated at: 20260929T060553Z - Config: `github-releases` - Servers: pyright, ty, pyrefly, pylsp-mypy - Baseline server: Pyright (pyright) @@ -14,7 +14,7 @@ Generated from `results/bench-servers/summary-20260928T060730Z.json` | --- | --- | --- | | Pyright | 1.1.414 | /home/runner/work/python-lsp-compare/python-lsp-compare/.python-lsp-compare/servers/pyright/1.1.414/package/dist/pyright-langserver.js | | Ty | 0.0.84 | /home/runner/work/python-lsp-compare/python-lsp-compare/.python-lsp-compare/servers/ty/0.0.84/ty-x86_64-unknown-linux-gnu/ty | -| Pyrefly | 1.3.1 | /home/runner/work/python-lsp-compare/python-lsp-compare/.python-lsp-compare/servers/pyrefly/venv/bin/pyrefly | +| Pyrefly | 1.3.2 | /home/runner/work/python-lsp-compare/python-lsp-compare/.python-lsp-compare/servers/pyrefly/venv/bin/pyrefly | | pylsp-mypy | 1.15.0 | /home/runner/work/python-lsp-compare/python-lsp-compare/.python-lsp-compare/servers/pylsp-mypy/venv/bin/pylsp | ## Server Notes @@ -30,10 +30,10 @@ Generated from `results/bench-servers/summary-20260928T060730Z.json` | Server | Success | Benchmarks | Wall clock ms | Avg measured ms | Measured requests | Non-empty % | Failed points | | --- | --- | --- | ---: | ---: | ---: | ---: | ---: | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 6 | 5088.02 | 4.16 | 150 | 100% | 0 | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | no | 8 | 16436.19 | 37.77 | 205 | 97% | 2 | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 6 | 38514.36 | 74.14 | 150 | 97% | 0 | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | no | 6 | 217566.56 | 364.51 | 150 | 80% | 5 | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 6 | 4105.65 | 3.45 | 150 | 100% | 0 | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | no | 8 | 11731.81 | 24.66 | 205 | 97% | 2 | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 6 | 28362.29 | 47.03 | 150 | 97% | 0 | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | no | 6 | 165606.66 | 284.74 | 150 | 80% | 5 | *Wall clock ms includes server startup, warmup iterations, and shutdown — but excludes one-time environment creation and dependency installation.* @@ -41,10 +41,10 @@ Generated from `results/bench-servers/summary-20260928T060730Z.json` | Server | Success | Wall clock ms | Avg measured ms | Points | Measured requests | Non-empty % | Failed points | | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 504.17 | 4.09 | 5 | 25 | 100% | 0 | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 1053.90 | 22.73 | 5 | 25 | 100% | 0 | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 4500.42 | 73.97 | 5 | 25 | 100% | 0 | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | no | 7688.41 | 114.99 | 5 | 25 | 80% | 1 | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 390.23 | 3.03 | 5 | 25 | 100% | 0 | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 819.48 | 17.23 | 5 | 25 | 100% | 0 | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 3302.06 | 51.11 | 5 | 25 | 100% | 0 | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | no | 6331.52 | 92.29 | 5 | 25 | 80% | 1 | ### dataframe completion @@ -52,10 +52,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 1.65 | 1.89 | 100% | 223.00 | +22.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 6.12 | 8.84 | 100% | 201.00 | 0.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 83.23 | 329.24 | 100% | 250.00 | +49.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 227.50 | 468.36 | 100% | 188.00 | -13.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 1.28 | 1.42 | 100% | 223.00 | +22.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 4.15 | 6.45 | 100% | 201.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 69.14 | 272.10 | 100% | 250.00 | +49.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 165.83 | 393.66 | 100% | 188.00 | -13.00 | pass | ### dataframe describe hover @@ -63,10 +63,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 0.33 | 0.36 | 100% | 4232.00 | +213.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 1.16 | 1.49 | 100% | 4019.00 | 0.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 2.23 | 2.33 | 100% | 3182.00 | -837.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 175.42 | 175.90 | 100% | 4134.00 | +115.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 0.23 | 0.24 | 100% | 4232.00 | +213.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 0.94 | 1.33 | 100% | 4019.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 1.82 | 1.94 | 100% | 3182.00 | -837.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 151.47 | 153.18 | 100% | 4134.00 | +115.00 | pass | ### summarize definition @@ -74,10 +74,10 @@ Method: `textDocument/definition` | Server | Success | Mean ms | P95 ms | Non-empty % | Definitions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 0.19 | 0.19 | 100% | 1.00 | 0.00 | pass | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 0.21 | 0.23 | 100% | 1.00 | 0.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 0.39 | 0.44 | 100% | 1.00 | 0.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 1.03 | 1.06 | 100% | 1.00 | 0.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 0.15 | 0.16 | 100% | 1.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 0.22 | 0.28 | 100% | 1.00 | 0.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 0.31 | 0.37 | 100% | 1.00 | 0.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 0.77 | 0.82 | 100% | 1.00 | 0.00 | pass | ### edit array then complete (edit+completion) @@ -85,10 +85,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | no | 4.29 | 4.36 | 0% | 0.00 | -168.00 | fail (10) | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 12.59 | 14.17 | 100% | 168.00 | 0.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 19.64 | 22.45 | 100% | 149.00 | -19.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 329.85 | 464.42 | 100% | 168.00 | 0.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | no | 3.39 | 3.54 | 0% | 0.00 | -168.00 | fail (10) | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 9.33 | 10.83 | 100% | 168.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 14.51 | 17.24 | 100% | 149.00 | -19.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 227.49 | 336.93 | 100% | 168.00 | 0.00 | pass | ### edit array then hover (edit+hover) @@ -96,10 +96,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 5.66 | 5.79 | 100% | 267.00 | -11.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 8.36 | 16.17 | 100% | 2546.00 | +2268.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 32.34 | 35.77 | 100% | 278.00 | 0.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 166.71 | 168.72 | 100% | 5662.00 | +5384.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 0.44 | 0.70 | 100% | 2546.00 | +2268.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 4.17 | 4.19 | 100% | 267.00 | -11.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 22.64 | 23.85 | 100% | 278.00 | 0.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 140.00 | 147.20 | 100% | 5662.00 | +5384.00 | pass | ### Result Differences @@ -112,10 +112,10 @@ Method: `textDocument/hover` | Server | Success | Wall clock ms | Avg measured ms | Points | Measured requests | Non-empty % | Failed points | | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 257.86 | 2.62 | 5 | 25 | 100% | 0 | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 285.29 | 4.25 | 5 | 25 | 100% | 0 | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 1461.00 | 13.95 | 5 | 25 | 100% | 0 | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 7736.99 | 172.00 | 5 | 25 | 100% | 0 | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 205.50 | 1.98 | 5 | 25 | 100% | 0 | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 206.08 | 2.84 | 5 | 25 | 100% | 0 | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 1137.26 | 11.00 | 5 | 25 | 100% | 0 | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 6039.29 | 134.50 | 5 | 25 | 100% | 0 | ### queryset completion @@ -123,10 +123,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 4.73 | 7.80 | 100% | 10.00 | 0.00 | pass | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 4.81 | 7.16 | 100% | 261.00 | +251.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 17.74 | 68.20 | 100% | 15.00 | +5.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 209.10 | 640.39 | 100% | 2.00 | -8.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 3.68 | 5.81 | 100% | 261.00 | +251.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 3.80 | 6.13 | 100% | 10.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 11.75 | 45.41 | 100% | 15.00 | +5.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 162.20 | 508.28 | 100% | 2.00 | -8.00 | pass | ### queryset filter hover @@ -134,10 +134,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 0.23 | 0.25 | 100% | 46.00 | -11.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 0.42 | 0.47 | 100% | 298.00 | +241.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 0.52 | 0.59 | 100% | 57.00 | 0.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 151.88 | 152.92 | 100% | 57.00 | 0.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 0.16 | 0.18 | 100% | 46.00 | -11.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 0.20 | 0.21 | 100% | 298.00 | +241.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 0.41 | 0.46 | 100% | 57.00 | 0.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 128.24 | 128.90 | 100% | 57.00 | 0.00 | pass | ### model definition @@ -145,10 +145,10 @@ Method: `textDocument/definition` | Server | Success | Mean ms | P95 ms | Non-empty % | Definitions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 0.19 | 0.20 | 100% | 1.00 | 0.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 0.37 | 0.41 | 100% | 1.00 | 0.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 0.37 | 0.47 | 100% | 1.00 | 0.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 1.08 | 1.11 | 100% | 1.00 | 0.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 0.13 | 0.13 | 100% | 1.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 0.16 | 0.17 | 100% | 1.00 | 0.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 0.30 | 0.34 | 100% | 1.00 | 0.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 0.75 | 0.78 | 100% | 1.00 | 0.00 | pass | ### edit queryset then complete (edit+completion) @@ -156,10 +156,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 1.94 | 2.02 | 100% | 83.00 | -21.00 | pass | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 4.73 | 5.04 | 100% | 104.00 | 0.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 25.85 | 28.51 | 100% | 104.00 | 0.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 255.91 | 289.24 | 100% | 143.00 | +39.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 1.49 | 1.57 | 100% | 83.00 | -21.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 3.56 | 3.71 | 100% | 104.00 | 0.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 20.27 | 21.67 | 100% | 104.00 | 0.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 189.08 | 228.11 | 100% | 143.00 | +39.00 | pass | ### edit queryset then hover (edit+hover) @@ -167,10 +167,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 0.79 | 0.85 | 100% | 858.00 | +775.00 | pass | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 3.16 | 3.20 | 100% | 100.00 | +17.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 38.27 | 43.63 | 100% | 83.00 | 0.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 242.02 | 243.47 | 100% | 71.00 | -12.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 0.61 | 0.63 | 100% | 858.00 | +775.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 2.40 | 2.41 | 100% | 100.00 | +17.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 30.20 | 35.72 | 100% | 83.00 | 0.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 192.22 | 194.27 | 100% | 71.00 | -12.00 | pass | ### Result Differences @@ -183,10 +183,10 @@ Method: `textDocument/hover` | Server | Success | Wall clock ms | Avg measured ms | Points | Measured requests | Non-empty % | Failed points | | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 832.64 | 7.82 | 5 | 25 | 100% | 0 | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 1129.21 | 29.57 | 5 | 25 | 100% | 0 | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 7632.92 | 136.30 | 5 | 25 | 100% | 0 | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 11274.25 | 185.99 | 5 | 25 | 100% | 0 | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 663.86 | 6.24 | 5 | 25 | 100% | 0 | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 852.25 | 21.32 | 5 | 25 | 100% | 0 | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 7157.04 | 86.18 | 5 | 25 | 100% | 0 | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 6286.28 | 106.88 | 5 | 25 | 100% | 0 | ### report dataframe completion @@ -194,10 +194,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 18.28 | 21.81 | 100% | 1000.00 | +728.80 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 79.07 | 266.94 | 100% | 271.20 | 0.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 87.86 | 347.62 | 100% | 16.00 | -255.20 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 92.23 | 216.42 | 100% | 6.00 | -265.20 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 14.11 | 17.17 | 100% | 1000.00 | +728.80 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 45.97 | 69.02 | 100% | 6.00 | -265.20 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 57.90 | 196.85 | 100% | 271.20 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 64.19 | 256.12 | 100% | 16.00 | -255.20 | pass | ### dataframe groupby hover @@ -205,10 +205,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 0.27 | 0.29 | 100% | 329.00 | -21.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 0.68 | 0.75 | 100% | 350.00 | 0.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 3.85 | 5.74 | 100% | 2759.00 | +2409.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 191.16 | 192.67 | 100% | 301.00 | -49.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 0.20 | 0.23 | 100% | 329.00 | -21.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 0.57 | 0.63 | 100% | 350.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 4.08 | 7.34 | 100% | 2759.00 | +2409.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 157.66 | 159.48 | 100% | 301.00 | -49.00 | pass | ### build report definition @@ -216,10 +216,10 @@ Method: `textDocument/definition` | Server | Success | Mean ms | P95 ms | Non-empty % | Definitions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 0.20 | 0.21 | 100% | 1.00 | 0.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 0.40 | 0.47 | 100% | 1.00 | 0.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 1.03 | 1.05 | 100% | 1.00 | 0.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 2.92 | 2.93 | 100% | 1.00 | 0.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 0.14 | 0.15 | 100% | 1.00 | 0.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 0.33 | 0.47 | 100% | 1.00 | 0.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 0.80 | 0.82 | 100% | 1.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 3.53 | 5.36 | 100% | 1.00 | 0.00 | pass | ### edit dataframe then complete (edit+completion) @@ -227,10 +227,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 15.86 | 17.41 | 100% | 448.00 | +8.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 47.65 | 80.12 | 100% | 256.00 | -184.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 215.62 | 216.90 | 100% | 441.00 | +1.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 838.49 | 1379.15 | 100% | 440.00 | 0.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 13.32 | 14.49 | 100% | 448.00 | +8.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 27.39 | 34.46 | 100% | 256.00 | -184.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 182.54 | 187.66 | 100% | 441.00 | +1.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 363.89 | 832.46 | 100% | 440.00 | 0.00 | pass | ### edit dataframe then hover (edit+hover) @@ -238,10 +238,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 4.47 | 4.48 | 100% | 4441.00 | +149.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 5.57 | 13.19 | 100% | 943.00 | -3349.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 11.32 | 13.29 | 100% | 4292.00 | 0.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 181.45 | 183.44 | 100% | 232.00 | -4060.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 3.43 | 3.46 | 100% | 4441.00 | +149.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 7.44 | 10.21 | 100% | 943.00 | -3349.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 8.22 | 10.27 | 100% | 4292.00 | 0.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 147.43 | 150.63 | 100% | 232.00 | -4060.00 | pass | ### Result Differences @@ -254,10 +254,10 @@ Method: `textDocument/hover` | Server | Success | Wall clock ms | Avg measured ms | Points | Measured requests | Non-empty % | Failed points | | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 374.41 | 2.88 | 5 | 25 | 100% | 0 | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 926.41 | 20.92 | 5 | 25 | 100% | 0 | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 3867.76 | 53.42 | 5 | 25 | 100% | 0 | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | no | 7086.84 | 120.64 | 5 | 25 | 60% | 2 | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 300.87 | 2.18 | 5 | 25 | 100% | 0 | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 765.03 | 17.67 | 5 | 25 | 100% | 0 | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 2925.76 | 39.92 | 5 | 25 | 100% | 0 | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | no | 5987.46 | 92.43 | 5 | 25 | 60% | 2 | ### query completion @@ -265,10 +265,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 3.33 | 7.41 | 100% | 1.00 | 0.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 14.42 | 26.58 | 100% | 1.00 | 0.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 88.10 | 351.56 | 100% | 15.00 | +14.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 208.83 | 460.44 | 100% | 1.00 | 0.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 2.60 | 6.00 | 100% | 1.00 | 0.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 15.68 | 20.85 | 100% | 1.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 64.92 | 259.08 | 100% | 15.00 | +14.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 153.24 | 420.86 | 100% | 1.00 | 0.00 | pass | ### sessionmaker hover @@ -276,10 +276,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 0.44 | 0.51 | 100% | 10621.00 | +42.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 2.49 | 3.32 | 100% | 15239.00 | +4660.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 4.25 | 7.79 | 100% | 10579.00 | 0.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 318.44 | 321.94 | 100% | 10498.00 | -81.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 0.30 | 0.31 | 100% | 10621.00 | +42.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 1.55 | 2.11 | 100% | 10579.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 2.92 | 3.44 | 100% | 15239.00 | +4660.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 248.87 | 253.96 | 100% | 10498.00 | -81.00 | pass | ### mapped class definition @@ -287,10 +287,10 @@ Method: `textDocument/definition` | Server | Success | Mean ms | P95 ms | Non-empty % | Definitions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 0.21 | 0.23 | 100% | 2.00 | +1.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 0.75 | 1.65 | 100% | 1.00 | 0.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 1.08 | 1.11 | 100% | 1.00 | 0.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 2.20 | 3.67 | 100% | 1.00 | 0.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 0.14 | 0.16 | 100% | 2.00 | +1.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 0.34 | 0.40 | 100% | 1.00 | 0.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 0.83 | 0.94 | 100% | 1.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 2.73 | 2.94 | 100% | 1.00 | 0.00 | pass | ### edit query then complete (edit+completion) @@ -298,10 +298,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 5.36 | 5.56 | 100% | 23.00 | -15.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 7.20 | 22.84 | 100% | 17.00 | -21.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | no | 37.64 | 38.27 | 0% | 0.00 | -38.00 | fail (10) | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 145.72 | 176.38 | 100% | 38.00 | 0.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 4.10 | 4.51 | 100% | 23.00 | -15.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 9.44 | 23.85 | 100% | 17.00 | -21.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | no | 29.67 | 30.00 | 0% | 0.00 | -38.00 | fail (10) | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 107.69 | 134.29 | 100% | 38.00 | 0.00 | pass | ### edit session then hover (edit+hover) @@ -309,10 +309,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 4.60 | 12.11 | 100% | 2242.00 | +1349.00 | pass | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 5.06 | 5.10 | 100% | 951.00 | +58.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | no | 37.18 | 37.55 | 0% | 0.00 | -893.00 | fail (10) | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 101.94 | 110.98 | 100% | 893.00 | 0.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 3.79 | 3.84 | 100% | 951.00 | +58.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 8.34 | 23.59 | 100% | 2242.00 | +1349.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | no | 29.53 | 30.42 | 0% | 0.00 | -893.00 | fail (10) | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 74.33 | 81.44 | 100% | 893.00 | 0.00 | pass | ### Result Differences @@ -326,10 +326,10 @@ Method: `textDocument/hover` | Server | Success | Wall clock ms | Avg measured ms | Points | Measured requests | Non-empty % | Failed points | | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 2761.66 | 4.78 | 5 | 25 | 100% | 0 | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 15845.34 | 108.00 | 5 | 25 | 80% | 0 | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 5609.51 | 178.76 | 5 | 25 | 80% | 0 | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | no | 182639.34 | 1538.47 | 5 | 25 | 40% | 2 | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 2234.16 | 4.67 | 5 | 25 | 100% | 0 | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 12671.18 | 86.95 | 5 | 25 | 80% | 0 | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 3904.83 | 119.91 | 5 | 25 | 80% | 0 | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | no | 137062.20 | 1200.13 | 5 | 25 | 40% | 2 | ### classifier pipeline completion @@ -337,10 +337,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 11.44 | 12.58 | 100% | 777.00 | +654.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 51.53 | 81.65 | 100% | 123.00 | 0.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 149.07 | 151.99 | 100% | 2.00 | -121.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 877.67 | 3509.79 | 100% | 15.00 | -108.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 11.06 | 12.12 | 100% | 777.00 | +654.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 46.54 | 70.06 | 100% | 123.00 | 0.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 102.33 | 104.37 | 100% | 2.00 | -121.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 591.03 | 2363.48 | 100% | 15.00 | -108.00 | pass | ### pipeline hover @@ -348,10 +348,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 0.22 | 0.22 | 100% | 48.00 | +14.00 | pass | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 0.25 | 0.27 | 100% | 7.00 | -27.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 0.48 | 0.54 | 100% | 34.00 | 0.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | no | 2741.08 | 2774.28 | 0% | 0.00 | -34.00 | fail (10) | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 0.14 | 0.17 | 100% | 48.00 | +14.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 0.15 | 0.16 | 100% | 7.00 | -27.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 0.38 | 0.46 | 100% | 34.00 | 0.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | no | 2152.58 | 2240.50 | 0% | 0.00 | -34.00 | fail (10) | ### auto tokenizer definition @@ -359,10 +359,10 @@ Method: `textDocument/definition` | Server | Success | Mean ms | P95 ms | Non-empty % | Definitions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 0.22 | 0.23 | 100% | 1.00 | 0.00 | pass | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 0.28 | 0.28 | 100% | 1.00 | 0.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 0.45 | 0.52 | 100% | 1.00 | 0.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 2180.97 | 2209.83 | 100% | 1.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 0.14 | 0.17 | 100% | 1.00 | 0.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 0.18 | 0.19 | 100% | 1.00 | 0.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 0.33 | 0.35 | 100% | 1.00 | 0.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 1731.78 | 1781.61 | 100% | 1.00 | 0.00 | pass | ### edit prediction then complete (edit+completion) @@ -370,10 +370,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 2.60 | 2.66 | 0% | 0.00 | 0.00 | pass | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 6.12 | 6.49 | 100% | 23.00 | +23.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 6.98 | 9.93 | 0% | 0.00 | 0.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 12.67 | 23.94 | 0% | 0.00 | 0.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 2.11 | 2.35 | 0% | 0.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 2.12 | 7.61 | 0% | 0.00 | 0.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 5.20 | 6.65 | 0% | 0.00 | 0.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 6.24 | 8.57 | 100% | 23.00 | +23.00 | pass | ### edit tokenizer then hover (edit+hover) @@ -381,10 +381,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 3.05 | 3.70 | 100% | 33.00 | +3.00 | pass | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 5.81 | 5.92 | 100% | 7.00 | -23.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 480.54 | 505.46 | 100% | 30.00 | 0.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | no | 2618.62 | 2658.80 | 0% | 0.00 | -30.00 | fail (10) | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 5.75 | 6.64 | 100% | 7.00 | -23.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 6.12 | 13.95 | 100% | 33.00 | +3.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 382.31 | 397.67 | 100% | 30.00 | 0.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | no | 2011.86 | 2032.58 | 0% | 0.00 | -30.00 | fail (10) | ### Result Differences @@ -397,10 +397,10 @@ Method: `textDocument/hover` | Server | Success | Wall clock ms | Avg measured ms | Points | Measured requests | Non-empty % | Failed points | | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 357.27 | 2.78 | 5 | 25 | 100% | 0 | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 1565.58 | 9.53 | 5 | 25 | 100% | 0 | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 864.84 | 12.83 | 5 | 25 | 100% | 0 | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 4782.06 | 104.69 | 5 | 25 | 100% | 0 | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 311.04 | 2.57 | 5 | 25 | 100% | 0 | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 1168.98 | 7.01 | 5 | 25 | 100% | 0 | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 681.26 | 10.00 | 5 | 25 | 100% | 0 | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 3899.92 | 82.22 | 5 | 25 | 100% | 0 | ### request args completion @@ -408,10 +408,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 4.71 | 7.83 | 100% | 14.00 | 0.00 | pass | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 5.74 | 8.14 | 100% | 467.00 | +453.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 54.96 | 182.88 | 100% | 487.80 | +473.80 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 70.75 | 96.72 | 100% | 1.00 | -13.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 3.61 | 6.61 | 100% | 14.00 | 0.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 5.27 | 7.37 | 100% | 467.00 | +453.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 41.28 | 134.26 | 100% | 487.80 | +473.80 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 59.36 | 123.69 | 100% | 1.00 | -13.00 | pass | ### client session hover @@ -419,10 +419,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 0.21 | 0.23 | 100% | 7.00 | -19.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 0.58 | 0.67 | 100% | 26.00 | 0.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 5.19 | 18.16 | 100% | 167.00 | +141.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 102.40 | 193.33 | 100% | 359.00 | +333.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 0.13 | 0.15 | 100% | 7.00 | -19.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 0.39 | 0.45 | 100% | 26.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 4.97 | 17.71 | 100% | 167.00 | +141.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 99.53 | 306.62 | 100% | 359.00 | +333.00 | pass | ### client references @@ -430,10 +430,10 @@ Method: `textDocument/references` | Server | Success | Mean ms | P95 ms | Non-empty % | References found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 0.34 | 0.35 | 100% | 2.00 | 0.00 | pass | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 0.58 | 0.62 | 100% | 2.00 | 0.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 0.81 | 0.85 | 100% | 2.00 | 0.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 33.42 | 84.02 | 100% | 2.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 0.25 | 0.29 | 100% | 2.00 | 0.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 0.47 | 0.56 | 100% | 2.00 | 0.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 1.05 | 2.51 | 100% | 2.00 | 0.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 3.02 | 4.00 | 100% | 2.00 | 0.00 | pass | ### edit response then complete (edit+completion) @@ -441,10 +441,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 0.58 | 0.62 | 100% | 32.00 | -173.00 | pass | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 4.44 | 4.57 | 100% | 225.00 | +20.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 4.80 | 5.89 | 100% | 205.00 | 0.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 88.43 | 88.72 | 100% | 57.00 | -148.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 0.42 | 0.44 | 100% | 32.00 | -173.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 3.82 | 4.43 | 100% | 225.00 | +20.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 3.91 | 5.13 | 100% | 205.00 | 0.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 62.77 | 64.23 | 100% | 57.00 | -148.00 | pass | ### edit response then hover (edit+hover) @@ -452,10 +452,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Ty](latest-results/ty-20260928T060730Z.json) | yes | 2.95 | 2.98 | 100% | 1555.00 | +1135.00 | pass | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 3.07 | 5.50 | 100% | 9977.00 | +9557.00 | pass | -| [Pyright](latest-results/pyright-20260928T060730Z.json) | yes | 36.75 | 39.79 | 100% | 420.00 | 0.00 | pass | -| [pylsp-mypy](latest-results/pylsp-mypy-20260928T060730Z.json) | yes | 228.47 | 230.60 | 100% | 880.00 | +460.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 3.08 | 4.69 | 100% | 9977.00 | +9557.00 | pass | +| [Ty](latest-results/ty-20260929T060553Z.json) | yes | 3.14 | 3.61 | 100% | 1555.00 | +1135.00 | pass | +| [Pyright](latest-results/pyright-20260929T060553Z.json) | yes | 26.10 | 29.43 | 100% | 420.00 | 0.00 | pass | +| [pylsp-mypy](latest-results/pylsp-mypy-20260929T060553Z.json) | yes | 186.41 | 188.66 | 100% | 880.00 | +460.00 | pass | ### Result Differences @@ -468,7 +468,7 @@ Method: `textDocument/hover` | Server | Success | Wall clock ms | Avg measured ms | Points | Measured requests | Non-empty % | Failed points | | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | no | 223.27 | 0.43 | 8 | 40 | 100% | 2 | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | no | 177.36 | 0.22 | 8 | 40 | 100% | 2 | ### builtins semantic tokens @@ -476,7 +476,7 @@ Method: semantic token impl using typeServer/getComputedType | Server | Success | Mean ms | P95 ms | Non-empty % | Semantic tokens found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 1.85 | 4.39 | 100% | 30.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 0.74 | 0.78 | 100% | 30.00 | 0.00 | pass | ### builtin int computed type @@ -484,7 +484,7 @@ Method: `typeServer/getComputedType` | Server | Success | Mean ms | P95 ms | Non-empty % | Results found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 0.21 | 0.22 | 100% | 7.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 0.13 | 0.14 | 100% | 7.00 | 0.00 | pass | ### list declared type @@ -492,7 +492,7 @@ Method: `typeServer/getDeclaredType` | Server | Success | Mean ms | P95 ms | Non-empty % | Results found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 0.24 | 0.25 | 100% | 7.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 0.16 | 0.17 | 100% | 7.00 | 0.00 | pass | ### generic specialization computed type @@ -500,7 +500,7 @@ Method: `typeServer/getComputedType` | Server | Success | Mean ms | P95 ms | Non-empty % | Results found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 0.25 | 0.26 | 100% | 7.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 0.16 | 0.18 | 100% | 7.00 | 0.00 | pass | ### stdlib path computed type @@ -508,7 +508,7 @@ Method: `typeServer/getComputedType` | Server | Success | Mean ms | P95 ms | Non-empty % | Results found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 0.21 | 0.21 | 100% | 7.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 0.16 | 0.19 | 100% | 7.00 | 0.00 | pass | ### function argument expected type @@ -516,13 +516,13 @@ Method: `typeServer/getExpectedType` | Server | Success | Mean ms | P95 ms | Non-empty % | Results found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 0.25 | 0.27 | 100% | 7.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 0.15 | 0.16 | 100% | 7.00 | 0.00 | pass | ## Benchmark: tsp_semantic | Server | Success | Wall clock ms | Avg measured ms | Points | Measured requests | Non-empty % | Failed points | | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 6343.76 | 66.56 | 3 | 15 | 100% | 0 | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 4325.51 | 21.44 | 3 | 15 | 100% | 0 | ### django semantic tokens @@ -530,7 +530,7 @@ Method: semantic token impl using typeServer/getComputedType | Server | Success | Mean ms | P95 ms | Non-empty % | Semantic tokens found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 17.65 | 36.64 | 100% | 126.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 9.57 | 18.50 | 100% | 126.00 | 0.00 | pass | ### transformers semantic tokens @@ -538,7 +538,7 @@ Method: semantic token impl using typeServer/getComputedType | Server | Success | Mean ms | P95 ms | Non-empty % | Semantic tokens found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 122.07 | 150.74 | 100% | 74.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 45.62 | 47.40 | 100% | 74.00 | 0.00 | pass | ### stdlib semantic tokens @@ -546,4 +546,4 @@ Method: semantic token impl using typeServer/getComputedType | Server | Success | Mean ms | P95 ms | Non-empty % | Semantic tokens found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| [Pyrefly](latest-results/pyrefly-20260928T060730Z.json) | yes | 59.96 | 73.59 | 100% | 75.00 | 0.00 | pass | +| [Pyrefly](latest-results/pyrefly-20260929T060553Z.json) | yes | 9.12 | 21.59 | 100% | 75.00 | 0.00 | pass | diff --git a/latest-results/pylsp-mypy-20260928T060730Z-responses.jsonl b/latest-results/pylsp-mypy-20260929T060553Z-responses.jsonl similarity index 100% rename from latest-results/pylsp-mypy-20260928T060730Z-responses.jsonl rename to latest-results/pylsp-mypy-20260929T060553Z-responses.jsonl diff --git a/latest-results/pylsp-mypy-20260928T060730Z.json b/latest-results/pylsp-mypy-20260929T060553Z.json similarity index 89% rename from latest-results/pylsp-mypy-20260928T060730Z.json rename to latest-results/pylsp-mypy-20260929T060553Z.json index 9abe3b0..a19b772 100644 --- a/latest-results/pylsp-mypy-20260928T060730Z.json +++ b/latest-results/pylsp-mypy-20260929T060553Z.json @@ -11,8 +11,8 @@ "transformers", "web" ], - "started_at_unix": 1790575782.7618036, - "finished_at_unix": 1790576000.4079301, + "started_at_unix": 1790662057.541394, + "finished_at_unix": 1790662223.2229986, "scenario_reports": [], "benchmark_reports": [ { @@ -25,7 +25,7 @@ "environment_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv", "python_executable": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/bin/python", "success": false, - "total_duration_ms": 7688.413252000004, + "total_duration_ms": 6331.515685999989, "error_message": null, "summary": { "request_count": 32, @@ -34,11 +34,11 @@ "failure_count": 0, "bytes_sent": 18656, "bytes_received": 310574, - "min_ms": 0.8288169999843831, - "max_ms": 1879.0587220000248, - "mean_ms": 239.30904103125084, - "median_ms": 159.56102049999288, - "p95_ms": 1280.9764288500016, + "min_ms": 0.7365070000560081, + "max_ms": 1547.6564010000402, + "mean_ms": 197.0274889999999, + "median_ms": 130.77451350000047, + "p95_ms": 1010.4964107999418, "result_summary": { "present_count": 31, "empty_count": 7, @@ -94,27 +94,27 @@ "by_method": { "textDocument/completion": { "point_count": 2, - "min_ms": 4.168798999984347, - "max_ms": 501.20564300002, - "mean_ms": 115.89076520000106, - "median_ms": 36.88116899999727, - "p95_ms": 427.308906500012 + "min_ms": 3.0839930000183813, + "max_ms": 455.768398000032, + "mean_ms": 84.60900030000857, + "median_ms": 25.016661500046666, + "p95_ms": 316.03119850005294 }, "textDocument/hover": { "point_count": 2, - "min_ms": 163.26257299999725, - "max_ms": 175.94233299999473, - "mean_ms": 171.06365410000137, - "median_ms": 171.7296109999893, - "p95_ms": 175.8566727999991 + "min_ms": 135.54597500001364, + "max_ms": 153.2853049999403, + "mean_ms": 145.73556500000677, + "median_ms": 149.16519000001927, + "p95_ms": 153.03793279998104 }, "textDocument/definition": { "point_count": 1, - "min_ms": 1.0183699999970486, - "max_ms": 1.056922000003624, - "mean_ms": 1.034802800000989, - "median_ms": 1.0276470000007976, - "p95_ms": 1.0557118000008359 + "min_ms": 0.7365070000560081, + "max_ms": 0.829115999977148, + "mean_ms": 0.7670243999882587, + "median_ms": 0.7507379999651675, + "p95_ms": 0.8180431999790017 } }, "validation": { @@ -140,11 +140,11 @@ "failure_count": 0, "bytes_sent": 1425, "bytes_received": 202685, - "min_ms": 69.40279299999474, - "max_ms": 501.20564300002, - "mean_ms": 227.49525020000192, - "median_ms": 155.8594679999885, - "p95_ms": 468.36264900001646, + "min_ms": 46.463740000035614, + "max_ms": 455.768398000032, + "mean_ms": 165.83094220002295, + "median_ms": 126.00305199998729, + "p95_ms": 393.6629760000414, "result_summary": { "present_count": 5, "empty_count": 0, @@ -204,9 +204,9 @@ { "kind": "request", "method": "textDocument/completion", - "duration_ms": 1589.7757290000243, + "duration_ms": 1279.7192459998996, "success": true, - "started_at_unix": 1790575783.0534687, + "started_at_unix": 1790662057.927289, "bytes_sent": 285, "bytes_received": 40537, "request_id": 2, @@ -234,9 +234,9 @@ { "kind": "request", "method": "textDocument/completion", - 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describe hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 6, "character": 19, "iteration": 1, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) __new__: def __new__(\n cls : type[DataFrame],\n data : DataFrame | Index | Iterable[Index | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | tuple[Hashable, ListLikeU]] | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | None = None,\n index : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n columns: ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n dtype : ExtensionDtype | dtype[generic] | str | type[bool | complex | object | str] | None = None,\n copy : bool | None = None\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/frame.pyi#L396,7) | [Index](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [Series](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/series.pyi#L341,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3627,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L555,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L566,7) | [ExtensionArray](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/arrays/base.pyi#L32,7) | [SequenceNotStr](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_typing.pyi#L100,7) | [range](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L4106,7) | [ExtensionDtype](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/dtypes/base.pyi#L11,7) | [generic](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L4968,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L932,7) | [object](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L114,7)"}}} -{"suite": "data_science", "label": "dataframe describe hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 6, "character": 19, "iteration": 2, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) __new__: def __new__(\n cls : type[DataFrame],\n data : DataFrame | Index | Iterable[Index | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | tuple[Hashable, ListLikeU]] | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | None = None,\n index : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n columns: ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n dtype : ExtensionDtype | dtype[generic] | str | type[bool | complex | object | str] | None = None,\n copy : bool | None = None\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/frame.pyi#L396,7) | [Index](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [Series](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/series.pyi#L341,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3627,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L555,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L566,7) | [ExtensionArray](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/arrays/base.pyi#L32,7) | [SequenceNotStr](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_typing.pyi#L100,7) | [range](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L4106,7) | [ExtensionDtype](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/dtypes/base.pyi#L11,7) | [generic](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L4968,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L932,7) | [object](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L114,7)"}}} -{"suite": "data_science", "label": "dataframe describe hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 6, "character": 19, "iteration": 3, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) __new__: def __new__(\n cls : type[DataFrame],\n data : DataFrame | Index | Iterable[Index | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | tuple[Hashable, ListLikeU]] | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | None = None,\n index : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n columns: ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n dtype : ExtensionDtype | dtype[generic] | str | type[bool | complex | object | str] | None = None,\n copy : bool | None = None\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/frame.pyi#L396,7) | [Index](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [Series](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/series.pyi#L341,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3627,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L555,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L566,7) | [ExtensionArray](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/arrays/base.pyi#L32,7) | [SequenceNotStr](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_typing.pyi#L100,7) | [range](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L4106,7) | [ExtensionDtype](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/dtypes/base.pyi#L11,7) | [generic](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L4968,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L932,7) | [object](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L114,7)"}}} -{"suite": "data_science", "label": "dataframe describe hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 6, "character": 19, "iteration": 4, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) __new__: def __new__(\n cls : type[DataFrame],\n data : DataFrame | Index | Iterable[Index | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | tuple[Hashable, ListLikeU]] | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | None = None,\n index : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n columns: ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n dtype : ExtensionDtype | dtype[generic] | str | type[bool | complex | object | str] | None = None,\n copy : bool | None = None\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/frame.pyi#L396,7) | [Index](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [Series](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/series.pyi#L341,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3627,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L555,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L566,7) | [ExtensionArray](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/arrays/base.pyi#L32,7) | [SequenceNotStr](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_typing.pyi#L100,7) | [range](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L4106,7) | [ExtensionDtype](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/dtypes/base.pyi#L11,7) | [generic](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L4968,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L932,7) | [object](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L114,7)"}}} -{"suite": "data_science", "label": "dataframe describe hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 6, "character": 19, "iteration": 5, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) __new__: def __new__(\n cls : type[DataFrame],\n data : DataFrame | Index | Iterable[Index | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | tuple[Hashable, ListLikeU]] | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | None = None,\n index : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n columns: ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n dtype : ExtensionDtype | dtype[generic] | str | type[bool | complex | object | str] | None = None,\n copy : bool | None = None\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/frame.pyi#L396,7) | [Index](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [Series](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/series.pyi#L341,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3627,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L555,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L566,7) | [ExtensionArray](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/arrays/base.pyi#L32,7) | [SequenceNotStr](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_typing.pyi#L100,7) | [range](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L4106,7) | [ExtensionDtype](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/dtypes/base.pyi#L11,7) | [generic](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L4968,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L932,7) | [object](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L114,7)"}}} +{"suite": "data_science", "label": "dataframe describe hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 6, "character": 19, "iteration": 1, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) __new__: def __new__(\n cls : type[DataFrame],\n data : DataFrame | Index | Iterable[Index | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | tuple[Hashable, ListLikeU]] | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | None = None,\n index : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n columns: ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n dtype : ExtensionDtype | dtype[generic] | str | type[bool | complex | object | str] | None = None,\n copy : bool | None = None\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/frame.pyi#L396,7) | [Index](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [Series](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/series.pyi#L341,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3627,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L555,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L566,7) | [ExtensionArray](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/arrays/base.pyi#L32,7) | [SequenceNotStr](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_typing.pyi#L100,7) | [range](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L4106,7) | [ExtensionDtype](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/dtypes/base.pyi#L11,7) | [generic](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L4968,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L932,7) | [object](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L114,7)"}}} +{"suite": "data_science", "label": "dataframe describe hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 6, "character": 19, "iteration": 2, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) __new__: def __new__(\n cls : type[DataFrame],\n data : DataFrame | Index | Iterable[Index | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | tuple[Hashable, ListLikeU]] | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | None = None,\n index : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n columns: ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n dtype : ExtensionDtype | dtype[generic] | str | type[bool | complex | object | str] | None = None,\n copy : bool | None = None\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/frame.pyi#L396,7) | [Index](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [Series](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/series.pyi#L341,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3627,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L555,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L566,7) | [ExtensionArray](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/arrays/base.pyi#L32,7) | [SequenceNotStr](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_typing.pyi#L100,7) | [range](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L4106,7) | [ExtensionDtype](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/dtypes/base.pyi#L11,7) | [generic](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L4968,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L932,7) | [object](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L114,7)"}}} +{"suite": "data_science", "label": "dataframe describe hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 6, "character": 19, "iteration": 3, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) __new__: def __new__(\n cls : type[DataFrame],\n data : DataFrame | Index | Iterable[Index | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | tuple[Hashable, ListLikeU]] | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | None = None,\n index : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n columns: ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n dtype : ExtensionDtype | dtype[generic] | str | type[bool | complex | object | str] | None = None,\n copy : bool | None = None\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/frame.pyi#L396,7) | [Index](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [Series](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/series.pyi#L341,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3627,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L555,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L566,7) | [ExtensionArray](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/arrays/base.pyi#L32,7) | [SequenceNotStr](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_typing.pyi#L100,7) | [range](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L4106,7) | [ExtensionDtype](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/dtypes/base.pyi#L11,7) | [generic](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L4968,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L932,7) | [object](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L114,7)"}}} +{"suite": "data_science", "label": "dataframe describe hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 6, "character": 19, "iteration": 4, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) __new__: def __new__(\n cls : type[DataFrame],\n data : DataFrame | Index | Iterable[Index | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | tuple[Hashable, ListLikeU]] | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | None = None,\n index : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n columns: ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n dtype : ExtensionDtype | dtype[generic] | str | type[bool | complex | object | str] | None = None,\n copy : bool | None = None\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/frame.pyi#L396,7) | [Index](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [Series](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/series.pyi#L341,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3627,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L555,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L566,7) | [ExtensionArray](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/arrays/base.pyi#L32,7) | [SequenceNotStr](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_typing.pyi#L100,7) | [range](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L4106,7) | [ExtensionDtype](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/dtypes/base.pyi#L11,7) | [generic](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L4968,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L932,7) | [object](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L114,7)"}}} +{"suite": "data_science", "label": "dataframe describe hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 6, "character": 19, "iteration": 5, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) __new__: def __new__(\n cls : type[DataFrame],\n data : DataFrame | Index | Iterable[Index | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | tuple[Hashable, ListLikeU]] | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | None = None,\n index : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n columns: ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n dtype : ExtensionDtype | dtype[generic] | str | type[bool | complex | object | str] | None = None,\n copy : bool | None = None\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/frame.pyi#L396,7) | [Index](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [Series](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/series.pyi#L341,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3627,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L555,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L566,7) | [ExtensionArray](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/arrays/base.pyi#L32,7) | [SequenceNotStr](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_typing.pyi#L100,7) | [range](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L4106,7) | [ExtensionDtype](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/dtypes/base.pyi#L11,7) | [generic](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L4968,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L932,7) | [object](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L114,7)"}}} {"suite": "data_science", "label": "summarize definition", "method": "textDocument/definition", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 10, "character": 15, "iteration": 1, "result": {"range": {"end": {"character": 13, "line": 4}, "start": {"character": 4, "line": 4}}, "uri": "file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py"}} {"suite": "data_science", "label": "summarize definition", "method": "textDocument/definition", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 10, "character": 15, "iteration": 2, "result": {"range": {"end": {"character": 13, "line": 4}, "start": {"character": 4, "line": 4}}, "uri": "file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py"}} {"suite": "data_science", "label": "summarize definition", "method": "textDocument/definition", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 10, "character": 15, "iteration": 3, "result": {"range": {"end": {"character": 13, "line": 4}, "start": {"character": 4, "line": 4}}, "uri": "file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py"}} @@ -18,21 +18,21 @@ {"suite": "data_science", "label": "edit array then complete (edit+completion)", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 10, "character": 31, "iteration": 3, "result": {"isIncomplete": false, "items": [{"detail": "ndarray", "kind": 5, "label": "T", "sortText": "0.9999.T"}, {"detail": "\n@overload\ndef all(\n self : ndarray,\n axis : None = None,\n out : None = None,\n keepdims: Literal[False] = False,\n *,\n where : _ArrayLikeBool_co = True\n) -> numpy.bool: ...\ndef all(\n self : ndarray,\n axis : int | tuple[int, ...],\n out : None = None,\n keepdims: Literal[False] = False,\n *,\n where : _ArrayLikeBool_co = True\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef all(\n self : ndarray,\n axis : int | tuple[int, ...] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n where : _ArrayLikeBool_co = True\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef all[ArrayT: ndarray](\n self : ndarray,\n axis : int | tuple[int, ...] | None = None,\n *,\n out : ArrayT,\n keepdims: builtins.bool = False,\n where : _ArrayLikeBool_co = True\n) -> ArrayT: ...\ndef all[ArrayT: ndarray](\n self : ndarray,\n axis : int | tuple[int, ...] | None,\n out : ArrayT,\n keepdims: builtins.bool = False,\n *,\n where : _ArrayLikeBool_co = True\n) -> ArrayT: ...", "kind": 2, "label": "all", "sortText": "0.9999.all"}, {"detail": "\n@overload\ndef any(\n self : ndarray,\n axis : None = None,\n out : None = None,\n keepdims: Literal[False] = False,\n *,\n where : _ArrayLikeBool_co = True\n) -> numpy.bool: ...\ndef any(\n self : ndarray,\n axis : int | tuple[int, ...],\n out : None = None,\n keepdims: Literal[False] = False,\n *,\n where : _ArrayLikeBool_co = True\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef any(\n self : ndarray,\n axis : int | tuple[int, ...] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n where : _ArrayLikeBool_co = True\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef any[ArrayT: ndarray](\n self : ndarray,\n axis : int | tuple[int, ...] | None = None,\n *,\n out : ArrayT,\n keepdims: builtins.bool = False,\n where : _ArrayLikeBool_co = True\n) -> ArrayT: ...\ndef any[ArrayT: ndarray](\n self : ndarray,\n axis : int | tuple[int, ...] | None,\n out : ArrayT,\n keepdims: builtins.bool = False,\n *,\n where : _ArrayLikeBool_co = True\n) -> ArrayT: ...", "kind": 2, "label": "any", "sortText": "0.9999.any"}, {"detail": "\n@overload\ndef argmax(\n self : ndarray,\n axis : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] = False\n) -> signedinteger[_NBitIntP]: ...\ndef argmax(\n self : ndarray,\n axis : SupportsIndex,\n out : None = None,\n *,\n keepdims: Literal[False] = False\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef argmax(\n self : ndarray,\n axis : SupportsIndex | None = None,\n out : None = None,\n *,\n keepdims: Literal[True]\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef argmax[ArrayT: ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]](\n self : ndarray,\n axis : SupportsIndex | None = None,\n *,\n out : ArrayT,\n keepdims: bool = False\n) -> ArrayT: ...\ndef argmax[ArrayT: ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]](\n self : ndarray,\n axis : SupportsIndex | None,\n out : ArrayT,\n *,\n keepdims: bool = False\n) -> ArrayT: ...", "kind": 2, "label": "argmax", "sortText": "0.9999.argmax"}, {"detail": "\n@overload\ndef argmin(\n self : ndarray,\n axis : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] = False\n) -> signedinteger[_NBitIntP]: ...\ndef argmin(\n self : ndarray,\n axis : SupportsIndex,\n out : None = None,\n *,\n keepdims: Literal[False] = False\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef argmin(\n self : ndarray,\n axis : SupportsIndex | None = None,\n out : None = None,\n *,\n keepdims: Literal[True]\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef argmin[ArrayT: ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]](\n self : ndarray,\n axis : SupportsIndex | None = None,\n *,\n out : ArrayT,\n keepdims: bool = False\n) -> ArrayT: ...\ndef argmin[ArrayT: ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]](\n self : ndarray,\n axis : SupportsIndex | None,\n out : ArrayT,\n *,\n keepdims: bool = False\n) -> ArrayT: ...", "kind": 2, "label": "argmin", "sortText": "0.9999.argmin"}, {"detail": "\n@overload\ndef argpartition(\n self : ndarray,\n kth : _ArrayLikeInt,\n /,\n axis : None,\n kind : Literal['introselect'] = 'introselect',\n order: None = None\n) -> ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]: ...\ndef argpartition(\n self : ndarray,\n kth : _ArrayLikeInt,\n /,\n axis : SupportsIndex = -1,\n kind : Literal['introselect'] = 'introselect',\n order: None = None\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef argpartition(\n self : ndarray[tuple[Any, ...], dtype[void]],\n kth : _ArrayLikeInt,\n /,\n axis : None,\n kind : Literal['introselect'] = 'introselect',\n order: Sequence[str] | str | None = None\n) -> ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]: ...\ndef argpartition(\n self : ndarray[tuple[Any, ...], dtype[void]],\n kth : _ArrayLikeInt,\n /,\n axis : SupportsIndex = -1,\n kind : Literal['introselect'] = 'introselect',\n order: Sequence[str] | str | None = None\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...", "kind": 2, "label": "argpartition", "sortText": "0.9999.argpartition"}, {"detail": "def argsort(\n self : ndarray,\n axis : SupportsIndex | None = ...,\n kind : Literal['H', 'M', 'Q', 'S', 'heap', 'heapsort', 'merge', 'mergesort', 'quick', 'quicksort', 'stable', 'stablesort'] | None = ...,\n order : Sequence[str] | str | None = ...,\n *,\n stable : bool | None = ...,\n descending: bool | None = ...\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...", "kind": 2, "label": "argsort", "sortText": "0.9999.argsort"}, {"detail": "\n@overload\ndef astype[ScalarT: generic](\n self : ndarray,\n dtype : _DTypeLike,\n order : _OrderKACF = ...,\n casting: _CastingKind = ...,\n subok : bool = ...,\n copy : _CopyMode | bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef astype(\n self : ndarray,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None,\n order : _OrderKACF = ...,\n casting: _CastingKind = ...,\n subok : bool = ...,\n copy : _CopyMode | bool = ...\n) -> ndarray: ...", "kind": 2, "label": "astype", "sortText": "0.9999.astype"}, {"detail": "ndarray | None", "kind": 5, "label": "base", "sortText": "0.9999.base"}, {"detail": "def byteswap(\n self : ndarray,\n inplace: bool = ...\n) -> ndarray: ...", "kind": 2, "label": "byteswap", "sortText": "0.9999.byteswap"}, {"detail": "\n@overload\ndef choose(\n self : ndarray,\n /,\n choices: ArrayLike,\n out : None = None,\n mode : _ModeKind = 'raise'\n) -> ndarray: ...\ndef choose[ArrayT: ndarray](\n self : ndarray,\n /,\n choices: ArrayLike,\n out : ArrayT,\n mode : _ModeKind = 'raise'\n) -> ArrayT: ...", "kind": 2, "label": "choose", "sortText": "0.9999.choose"}, {"detail": "\n@overload\ndef clip(\n self : ndarray,\n /,\n min : ArrayLike,\n max : Buffer | _NestedSequence[bytes | complex | str] | _NestedSequence[_SupportsArray[dtype]] | _SupportsArray[dtype] | bytes | complex | str | None = None,\n out : None = None,\n **kwargs: Any\n) -> ndarray: ...\ndef clip(\n self : ndarray,\n /,\n min : None,\n max : ArrayLike,\n out : None = None,\n **kwargs: Any\n) -> ndarray: ...\ndef clip(\n self : ndarray,\n /,\n min : None = None,\n *,\n max : ArrayLike,\n out : None = None,\n **kwargs: Any\n) -> ndarray: ...\ndef clip[ArrayT: ndarray](\n self: ndarray,\n /,\n min: ArrayLike,\n max: Buffer | _NestedSequence[bytes | complex | str] | _NestedSequence[_SupportsArray[dtype]] | _SupportsArray[dtype] | bytes | complex | str | None,\n out: ArrayT,\n **kwargs: Any\n) -> ArrayT: ...\ndef clip[ArrayT: ndarray](\n self : ndarray,\n /,\n min : ArrayLike,\n max : Buffer | _NestedSequence[bytes | complex | str] | _NestedSequence[_SupportsArray[dtype]] | _SupportsArray[dtype] | bytes | complex | str | None = None,\n *,\n out : ArrayT,\n **kwargs: Any\n) -> ArrayT: ...\ndef clip[ArrayT: ndarray](\n self: ndarray,\n /,\n min: None,\n max: ArrayLike,\n out: ArrayT,\n **kwargs: Any\n) -> ArrayT: ...\ndef clip[ArrayT: ndarray](\n self : ndarray,\n /,\n min : None = None,\n *,\n max : ArrayLike,\n out : ArrayT,\n **kwargs: Any\n) -> ArrayT: ...", "kind": 2, "label": "clip", "sortText": "0.9999.clip"}, {"detail": "\n@overload\ndef compress(\n self : ndarray,\n /,\n condition: _ArrayLikeInt_co,\n axis : SupportsIndex | None = None,\n out : None = None\n) -> ndarray: ...\ndef compress[ArrayT: ndarray](\n self: ndarray,\n /,\n condition: _ArrayLikeInt_co,\n axis: SupportsIndex | None,\n out: ArrayT\n) -> ArrayT: ...\ndef compress[ArrayT: ndarray](\n self : ndarray,\n /,\n condition: _ArrayLikeInt_co,\n axis : SupportsIndex | None = None,\n *,\n out : ArrayT\n) -> ArrayT: ...", "kind": 2, "label": "compress", "sortText": "0.9999.compress"}, {"detail": "def conj(self: ndarray) -> ndarray: ...", "kind": 2, "label": "conj", "sortText": "0.9999.conj"}, {"detail": "def conjugate(self: ndarray) -> ndarray: ...", "kind": 2, "label": "conjugate", "sortText": "0.9999.conjugate"}, {"detail": "def copy(\n self : ndarray,\n order: _OrderKACF = ...\n) -> ndarray: ...", "kind": 2, "label": "copy", "sortText": "0.9999.copy"}, {"detail": "_ctypes[int]", "kind": 5, "label": "ctypes", "sortText": "0.9999.ctypes"}, {"detail": "\n@overload\ndef cumprod[DTypeT: dtype[inexact | object_]](\n self : ndarray[Any, DTypeT],\n axis : None = None,\n dtype: None = None,\n out : None = None\n) -> ndarray[tuple[int], DTypeT]: ...\ndef cumprod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : None = None,\n dtype: None = None,\n out : None = None\n) -> ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]: ...\ndef cumprod[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : None = None,\n *,\n dtype: _DTypeLike,\n out : None = None\n) -> ndarray[tuple[int], dtype[ScalarT]]: ...\ndef cumprod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : None = None,\n *,\n dtype: DTypeLike,\n out : None = None\n) -> ndarray[tuple[int]]: ...\ndef cumprod[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : None,\n dtype: _DTypeLike,\n out : None = None\n) -> ndarray[tuple[int], dtype[ScalarT]]: ...\ndef cumprod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : None,\n dtype: DTypeLike,\n out : None = None\n) -> ndarray[tuple[int]]: ...\ndef cumprod[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | object_]]](\n self : ArrayT,\n axis : SupportsIndex,\n dtype: None = None,\n out : None = None\n) -> ArrayT: ...\ndef cumprod[ShapeT: tuple[int, ...]](\n self : ndarray[ShapeT, dtype[numpy.bool | integer]],\n axis : SupportsIndex,\n dtype: None = None,\n out : None = None\n) -> ndarray[ShapeT, dtype[signedinteger[_NBitIntP]]]: ...\ndef cumprod[ShapeT: tuple[int, ...], ScalarT: generic](\n self : ndarray[ShapeT, dtype[numpy.bool | number | object_]],\n axis : SupportsIndex,\n dtype: _DTypeLike,\n out : None = None\n) -> ndarray[ShapeT, dtype[ScalarT]]: ...\ndef cumprod[ShapeT: tuple[int, ...]](\n self : ndarray[ShapeT, dtype[numpy.bool | number | object_]],\n axis : SupportsIndex,\n dtype: DTypeLike,\n out : None = None\n) -> ndarray[ShapeT]: ...\ndef cumprod[ArrayT: ndarray](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis: SupportsIndex | None,\n dtype: _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None,\n out: ArrayT\n) -> ArrayT: ...\ndef cumprod[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : SupportsIndex | None = None,\n dtype: _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT\n) -> ArrayT: ...", "kind": 2, "label": "cumprod", "sortText": "0.9999.cumprod"}, {"detail": "\n@overload\ndef cumsum[DTypeT: dtype[inexact | object_ | timedelta64]](\n self : ndarray[Any, DTypeT],\n axis : None = None,\n dtype: None = None,\n out : None = None\n) -> ndarray[tuple[int], DTypeT]: ...\ndef cumsum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : None = None,\n dtype: None = None,\n out : None = None\n) -> ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]: ...\ndef cumsum[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n *,\n dtype: _DTypeLike,\n out : None = None\n) -> ndarray[tuple[int], dtype[ScalarT]]: ...\ndef cumsum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n *,\n dtype: DTypeLike,\n out : None = None\n) -> ndarray[tuple[int]]: ...\ndef cumsum[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None,\n dtype: _DTypeLike,\n out : None = None\n) -> ndarray[tuple[int], dtype[ScalarT]]: ...\ndef cumsum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None,\n dtype: DTypeLike,\n out : None = None\n) -> ndarray[tuple[int]]: ...\ndef cumsum[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | object_ | timedelta64]]](\n self : ArrayT,\n axis : SupportsIndex,\n dtype: None = None,\n out : None = None\n) -> ArrayT: ...\ndef cumsum[ShapeT: tuple[int, ...]](\n self : ndarray[ShapeT, dtype[numpy.bool | integer]],\n axis : SupportsIndex,\n dtype: None = None,\n out : None = None\n) -> ndarray[ShapeT, dtype[signedinteger[_NBitIntP]]]: ...\ndef cumsum[ShapeT: tuple[int, ...], ScalarT: generic](\n self : ndarray[ShapeT, dtype[numpy.bool | number | object_ | timedelta64]],\n axis : SupportsIndex,\n dtype: _DTypeLike,\n out : None = None\n) -> ndarray[ShapeT, dtype[ScalarT]]: ...\ndef cumsum[ShapeT: tuple[int, ...]](\n self : ndarray[ShapeT, dtype[numpy.bool | number | object_ | timedelta64]],\n axis : SupportsIndex,\n dtype: DTypeLike,\n out : None = None\n) -> ndarray[ShapeT]: ...\ndef cumsum[ArrayT: ndarray](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis: SupportsIndex | None,\n dtype: _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None,\n out: ArrayT\n) -> ArrayT: ...\ndef cumsum[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : SupportsIndex | None = None,\n dtype: _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT\n) -> ArrayT: ...", "kind": 2, "label": "cumsum", "sortText": "0.9999.cumsum"}, {"detail": "memoryview", "kind": 5, "label": "data", "sortText": "0.9999.data"}, {"detail": "Literal['cpu']", "kind": 5, "label": "device", "sortText": "0.9999.device"}, {"detail": "\n@overload\ndef diagonal[DTypeT: dtype](\n self : ndarray[tuple[Never, Never, Never, Never], DTypeT],\n offset: SupportsIndex = 0,\n axis1 : SupportsIndex = 0,\n axis2 : SupportsIndex = 1\n) -> ndarray[tuple[Any, ...], DTypeT]: ...\ndef diagonal[DTypeT: dtype](\n self : ndarray[tuple[int, int], DTypeT],\n offset: SupportsIndex = 0,\n axis1 : SupportsIndex = 0,\n axis2 : SupportsIndex = 1\n) -> ndarray[tuple[int], DTypeT]: ...\ndef diagonal[DTypeT: dtype](\n self : ndarray[tuple[int, int, int], DTypeT],\n offset: SupportsIndex = 0,\n axis1 : SupportsIndex = 0,\n axis2 : SupportsIndex = 1\n) -> ndarray[tuple[int, int], DTypeT]: ...\ndef diagonal(\n self : ndarray,\n offset: SupportsIndex = 0,\n axis1 : SupportsIndex = 0,\n axis2 : SupportsIndex = 1\n) -> ndarray: ...", "kind": 2, "label": "diagonal", "sortText": "0.9999.diagonal"}, {"detail": "\n@overload\ndef dot(\n self: ndarray,\n b : _ScalarLike_co,\n /,\n out : None = None\n) -> ndarray: ...\ndef dot(\n self: ndarray,\n b : ArrayLike,\n /,\n out : None = None\n) -> Any: ...\ndef dot[ArrayT: ndarray](\n self: ndarray,\n b: ArrayLike,\n /,\n out: ArrayT\n) -> ArrayT: ...", "kind": 2, "label": "dot", "sortText": "0.9999.dot"}, {"detail": "dtype", "kind": 5, "label": "dtype", "sortText": "0.9999.dtype"}, {"detail": "def dump(\n self: ndarray,\n file: PathLike[bytes] | PathLike[str] | SupportsWrite[bytes] | bytes | str\n) -> None: ...", "kind": 2, "label": "dump", "sortText": "0.9999.dump"}, {"detail": "def dumps(self: ndarray) -> bytes: ...", "kind": 2, "label": "dumps", "sortText": "0.9999.dumps"}, {"detail": "def fill(\n self: ndarray,\n /,\n value: Any\n) -> None: ...", "kind": 2, "label": "fill", "sortText": "0.9999.fill"}, {"detail": "flagsobj", "kind": 5, "label": "flags", "sortText": "0.9999.flags"}, {"detail": "flatiter", "kind": 5, "label": "flat", "sortText": "0.9999.flat"}, {"detail": "def flatten(\n self : ndarray,\n /,\n order: _OrderKACF = 'C'\n) -> ndarray[tuple[int]]: ...", "kind": 2, "label": "flatten", "sortText": "0.9999.flatten"}, {"detail": "\n@overload\ndef getfield[ScalarT: generic](\n self : ndarray,\n /,\n dtype : _DTypeLike,\n offset: SupportsIndex = 0\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef getfield(\n self : ndarray,\n /,\n dtype : DTypeLike,\n offset: SupportsIndex = 0\n) -> ndarray: ...", "kind": 2, "label": "getfield", "sortText": "0.9999.getfield"}, {"detail": "ndarray", "kind": 5, "label": "imag", "sortText": "0.9999.imag"}, {"detail": "\n@overload\ndef item[T](\n self : ndarray[tuple[Any, ...], dtype[generic[T]]],\n i0 : SupportsIndex | tuple[SupportsIndex, ...] = ...,\n /,\n *args: SupportsIndex\n) -> T: ...\ndef item(\n self : ndarray[Any, StringDType],\n arg0 : SupportsIndex | tuple[SupportsIndex, ...] = ...,\n /,\n *args: SupportsIndex\n) -> str: ...", "kind": 2, "label": "item", "sortText": "0.9999.item"}, {"detail": "int", "kind": 5, "label": "itemsize", "sortText": "0.9999.itemsize"}, {"detail": "ndarray", "kind": 5, "label": "mT", "sortText": "0.9999.mT"}, {"detail": "\n@overload\ndef max[ScalarT: numpy.bool | datetime64 | number | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef max[ScalarT: numpy.bool | datetime64 | number | object_ | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef max[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool | datetime64 | number | object_ | timedelta64]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef max(\n self : ndarray[tuple[Any, ...], dtype[object_]],\n axis : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef max[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | datetime64 | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n *,\n out : ArrayT,\n keepdims: _NoValueType | builtins.bool = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...", "kind": 2, "label": "max", "sortText": "0.9999.max"}, {"detail": "\n@overload\ndef mean(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer | object_]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> float64: ...\ndef mean(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef mean(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef mean[ScalarT: inexact | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef mean[ScalarT: inexact | object_ | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef mean[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | object_ | timedelta64]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef mean[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef mean[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n keepdims: Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef mean[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None,\n dtype : _DTypeLike,\n out : None = None,\n *,\n keepdims: Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef mean[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _DTypeLike,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef mean[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT,\n keepdims: _NoValueType | builtins.bool = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef mean(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef mean(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray: ...\ndef mean(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray: ...", "kind": 2, "label": "mean", "sortText": "0.9999.mean"}, {"detail": "\n@overload\ndef min[ScalarT: numpy.bool | datetime64 | number | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef min[ScalarT: numpy.bool | datetime64 | number | object_ | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef min[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool | datetime64 | number | object_ | timedelta64]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef min(\n self : ndarray[tuple[Any, ...], dtype[object_]],\n axis : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef min[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | datetime64 | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n *,\n out : ArrayT,\n keepdims: _NoValueType | builtins.bool = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...", "kind": 2, "label": "min", "sortText": "0.9999.min"}, {"detail": "int", "kind": 5, "label": "nbytes", "sortText": "0.9999.nbytes"}, {"detail": "int", "kind": 5, "label": "ndim", "sortText": "0.9999.ndim"}, {"detail": "\n@overload\ndef nonzero(self: ndarray[tuple[Never, Never, Never, Never]]) -> tuple[ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]], ...]: ...\ndef nonzero(self: ndarray[tuple[int]]) -> tuple[ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]]: ...\ndef nonzero(self: ndarray[tuple[int, int]]) -> tuple[ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]], ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]]: ...\ndef nonzero(self: ndarray[tuple[int, int, int]]) -> tuple[ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]], ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]], ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]]: ...\ndef nonzero(self: ndarray) -> tuple[ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]], ...]: ...", "kind": 2, "label": "nonzero", "sortText": "0.9999.nonzero"}, {"detail": "\n@overload\ndef partition(\n self : ndarray,\n kth : _ArrayLikeInt,\n /,\n axis : SupportsIndex = -1,\n kind : Literal['introselect'] = 'introselect',\n order: None = None\n) -> None: ...\ndef partition(\n self : ndarray[tuple[Any, ...], dtype[void]],\n kth : _ArrayLikeInt,\n /,\n axis : SupportsIndex = -1,\n kind : Literal['introselect'] = 'introselect',\n order: Sequence[str] | str | None = None\n) -> None: ...", "kind": 2, "label": "partition", "sortText": "0.9999.partition"}, {"detail": "\n@overload\ndef prod[ScalarT: inexact](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef prod[ScalarT: inexact | object_](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef prod[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | object_]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | int | integer = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> signedinteger[_NBitIntP]: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | int | integer = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | int | integer = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[object_]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef prod[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef prod[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...] | None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef prod[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...] | None,\n dtype : _DTypeLike,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef prod[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...],\n dtype : _DTypeLike,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef prod[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT,\n keepdims: _NoValueType | builtins.bool = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef prod[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...] | None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None,\n out : ArrayT,\n *,\n keepdims: _NoValueType | builtins.bool = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...],\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray: ...", "kind": 2, "label": "prod", "sortText": "0.9999.prod"}, {"detail": "def put(\n self : ndarray,\n indices: _ArrayLikeInt_co,\n values : ArrayLike,\n /,\n mode : _ModeKind = 'raise'\n) -> None: ...", "kind": 2, "label": "put", "sortText": "0.9999.put"}, {"detail": "def ravel(\n self : ndarray,\n /,\n order: _OrderKACF = 'C'\n) -> ndarray[tuple[int]]: ...", "kind": 2, "label": "ravel", "sortText": "0.9999.ravel"}, {"detail": "ndarray", "kind": 5, "label": "real", "sortText": "0.9999.real"}, {"detail": "\n@overload\ndef repeat(\n self : ndarray,\n repeats: _ArrayLikeInt_co,\n /,\n axis : None = None\n) -> ndarray[tuple[int]]: ...\ndef repeat(\n self: ndarray,\n repeats: _ArrayLikeInt_co,\n /,\n axis: SupportsIndex\n) -> ndarray: ...", "kind": 2, "label": "repeat", "sortText": "0.9999.repeat"}, {"detail": "\n@overload\ndef reshape(\n self : ndarray,\n shape: None,\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray: ...\ndef reshape(\n self : ndarray,\n shape: Sequence[Never],\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray[tuple[()]]: ...\ndef reshape[AnyShapeT: (tuple[()], tuple[int], tuple[int, int], tuple[int, int, int], tuple[int, int, int, int], tuple[int, int, int, int, int], tuple[int, int, int, int, int, int], tuple[int, int, int, int, int, int, int], tuple[int, int, int, int, int, int, int, int])](\n self : ndarray,\n shape: AnyShapeT,\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray[AnyShapeT]: ...\ndef reshape(\n self : ndarray,\n size1: SupportsIndex,\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray[tuple[int]]: ...\ndef reshape(\n self : ndarray,\n size1: SupportsIndex,\n size2: SupportsIndex,\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray[tuple[int, int]]: ...\ndef reshape(\n self : ndarray,\n size1: SupportsIndex,\n size2: SupportsIndex,\n size3: SupportsIndex,\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray[tuple[int, int, int]]: ...\ndef reshape(\n self : ndarray,\n size1: SupportsIndex,\n size2: SupportsIndex,\n size3: SupportsIndex,\n size4: SupportsIndex,\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray[tuple[int, int, int, int]]: ...\ndef reshape(\n self : ndarray,\n size0 : SupportsIndex,\n /,\n *shape: SupportsIndex,\n *,\n order : _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray: ...\ndef reshape(\n self : ndarray,\n shape: Sequence[SupportsIndex],\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray: ...", "kind": 2, "label": "reshape", "sortText": "0.9999.reshape"}, {"detail": "\n@overload\ndef resize(\n self : ndarray,\n new_shape: _ShapeLike,\n /,\n *,\n refcheck : bool = True\n) -> None: ...\ndef resize(\n self : ndarray,\n /,\n *new_shape: SupportsIndex,\n *,\n refcheck : bool = True\n) -> None: ...", "kind": 2, "label": "resize", "sortText": "0.9999.resize"}, {"detail": "\n@overload\ndef round(\n self : ndarray,\n /,\n decimals: SupportsIndex = 0,\n out : None = None\n) -> ndarray: ...\ndef round[ArrayT: ndarray](\n self: ndarray,\n /,\n decimals: SupportsIndex,\n out: ArrayT\n) -> ArrayT: ...\ndef round[ArrayT: ndarray](\n self : ndarray,\n /,\n decimals: SupportsIndex = 0,\n *,\n out : ArrayT\n) -> ArrayT: ...", "kind": 2, "label": "round", "sortText": "0.9999.round"}, {"detail": "\n@overload\ndef searchsorted(\n self : ndarray,\n v : _ScalarLike_co,\n /,\n side : _SortSide = 'left',\n sorter: _NestedSequence[_SupportsArray[dtype[numpy.bool | integer]]] | _NestedSequence[int] | _SupportsArray[dtype[numpy.bool | integer]] | int | None = None\n) -> signedinteger[_NBitIntP]: ...\ndef searchsorted(\n self : ndarray,\n v : ArrayLike,\n /,\n side : _SortSide = 'left',\n sorter: _NestedSequence[_SupportsArray[dtype[numpy.bool | integer]]] | _NestedSequence[int] | _SupportsArray[dtype[numpy.bool | integer]] | int | None = None\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...", "kind": 2, "label": "searchsorted", "sortText": "0.9999.searchsorted"}, {"detail": "def setfield(\n self : ndarray,\n val : ArrayLike,\n /,\n dtype : DTypeLike,\n offset: SupportsIndex = 0\n) -> None: ...", "kind": 2, "label": "setfield", "sortText": "0.9999.setfield"}, {"detail": "def setflags(\n self : ndarray,\n /,\n *,\n write: bool | None = None,\n align: bool | None = None,\n uic : bool | None = None\n) -> None: ...", "kind": 2, "label": "setflags", "sortText": "0.9999.setflags"}, {"detail": "tuple[Any, ...]", "kind": 5, "label": "shape", "sortText": "0.9999.shape"}, {"detail": "int", "kind": 5, "label": "size", "sortText": "0.9999.size"}, {"detail": "def sort(\n self : ndarray,\n /,\n axis : SupportsIndex = -1,\n kind : Literal['H', 'M', 'Q', 'S', 'heap', 'heapsort', 'merge', 'mergesort', 'quick', 'quicksort', 'stable', 'stablesort'] | None = None,\n order : Sequence[str] | str | None = None,\n *,\n stable : bool | None = None,\n descending: bool | None = None\n) -> None: ...", "kind": 2, "label": "sort", "sortText": "0.9999.sort"}, {"detail": "def squeeze(\n self: ndarray,\n /,\n axis: SupportsIndex | tuple[SupportsIndex, ...] | None = ...\n) -> ndarray: ...", "kind": 2, "label": "squeeze", "sortText": "0.9999.squeeze"}, {"detail": "\n@overload\ndef std(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | complex128 | integer | object_]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> float64: ...\ndef std(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | complex128 | integer]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef std(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | complex128 | integer]],\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef std[ScalarT: floating | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ScalarT: ...\ndef std[ScalarT: floating | object_ | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef std[ArrayT: ndarray[tuple[Any, ...], dtype[floating | object_ | timedelta64]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ArrayT: ...\ndef std[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ScalarT: ...\ndef std[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef std[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None,\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef std[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef std[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT,\n ddof : float = 0,\n keepdims : _NoValueType | builtins.bool = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ArrayT: ...\ndef std(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> Any: ...\ndef std(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray: ...\ndef std(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray: ...", "kind": 2, "label": "std", "sortText": "0.9999.std"}, {"detail": "tuple[int, ...]", "kind": 5, "label": "strides", "sortText": "0.9999.strides"}, {"detail": "\n@overload\ndef sum[ScalarT: inexact | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef sum[ScalarT: inexact | object_ | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef sum[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | object_ | timedelta64]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | int | integer = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> signedinteger[_NBitIntP]: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | int | integer = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | int | integer = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[object_]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef sum[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef sum[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef sum[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None,\n dtype : _DTypeLike,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef sum[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _DTypeLike,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef sum[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT,\n keepdims: _NoValueType | builtins.bool = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef sum[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None,\n out : ArrayT,\n *,\n keepdims: _NoValueType | builtins.bool = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray: ...", "kind": 2, "label": "sum", "sortText": "0.9999.sum"}, {"detail": "def swapaxes(\n self: ndarray,\n axis1: SupportsIndex,\n axis2: SupportsIndex,\n /\n) -> ndarray: ...", "kind": 2, "label": "swapaxes", "sortText": "0.9999.swapaxes"}, {"detail": "\n@overload\ndef take[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n indices: _IntLike_co,\n /,\n axis : SupportsIndex | None = ...,\n out : None = None,\n mode : _ModeKind = ...\n) -> ScalarT: ...\ndef take(\n self : ndarray,\n indices: _ArrayLikeInt_co,\n /,\n axis : SupportsIndex | None = ...,\n out : None = None,\n mode : _ModeKind = ...\n) -> ndarray: ...\ndef take[ArrayT: ndarray](\n self : ndarray,\n indices: _ArrayLikeInt_co,\n /,\n axis : SupportsIndex | None = ...,\n *,\n out : ArrayT,\n mode : _ModeKind = ...\n) -> ArrayT: ...\ndef take[ArrayT: ndarray](\n self : ndarray,\n indices: _ArrayLikeInt_co,\n /,\n axis : SupportsIndex | None,\n out : ArrayT,\n mode : _ModeKind = ...\n) -> ArrayT: ...", "kind": 2, "label": "take", "sortText": "0.9999.take"}, {"detail": "def to_device(\n self : ndarray,\n device: Literal['cpu'],\n /,\n *,\n stream: int | Any | None = ...\n) -> ndarray: ...", "kind": 2, "label": "to_device", "sortText": "0.9999.to_device"}, {"detail": "def tobytes(\n self : ndarray,\n order: _OrderKACF = ...\n) -> bytes: ...", "kind": 2, "label": "tobytes", "sortText": "0.9999.tobytes"}, {"detail": "def tofile(\n self : ndarray,\n fid : PathLike[bytes] | PathLike[str] | _SupportsFileMethods | bytes | str,\n /,\n sep : str = '',\n format: str = '%s'\n) -> None: ...", "kind": 2, "label": "tofile", "sortText": "0.9999.tofile"}, {"detail": "\n@overload\ndef tolist[T](self: ndarray[tuple[Never], dtype[generic[T]]], /) -> Any: ...\ndef tolist[T](self: ndarray[tuple[()], dtype[generic[T]]], /) -> T: ...\ndef tolist[T](self: ndarray[tuple[int], dtype[generic[T]]], /) -> list[T]: ...\ndef tolist[T](self: ndarray[tuple[int, int], dtype[generic[T]]], /) -> list[list[T]]: ...\ndef tolist[T](self: ndarray[tuple[int, int, int], dtype[generic[T]]], /) -> list[list[list[T]]]: ...\ndef tolist(self: ndarray, /) -> Any: ...", "kind": 2, "label": "tolist", "sortText": "0.9999.tolist"}, {"detail": "\n@overload\ndef trace(\n self : ndarray,\n /,\n offset: SupportsIndex = 0,\n axis1 : SupportsIndex = 0,\n axis2 : SupportsIndex = 1,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None\n) -> Any: ...\ndef trace[ArrayT: ndarray](\n self : ndarray,\n /,\n offset: SupportsIndex = 0,\n axis1 : SupportsIndex = 0,\n axis2 : SupportsIndex = 1,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT\n) -> ArrayT: ...\ndef trace[ArrayT: ndarray](\n self: ndarray,\n /,\n offset: SupportsIndex,\n axis1: SupportsIndex,\n axis2: SupportsIndex,\n dtype: _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None,\n out: ArrayT\n) -> ArrayT: ...", "kind": 2, "label": "trace", "sortText": "0.9999.trace"}, {"detail": "\n@overload\ndef transpose(\n self: ndarray,\n axes: Sequence[SupportsIndex] | SupportsIndex | None,\n /\n) -> ndarray: ...\ndef transpose(\n self: ndarray,\n /,\n *axes: SupportsIndex\n) -> ndarray: ...", "kind": 2, "label": "transpose", "sortText": "0.9999.transpose"}, {"detail": "\n@overload\ndef var(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | complex128 | integer | object_]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> float64: ...\ndef var(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | complex128 | integer]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef var(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | complex128 | integer]],\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef var[ScalarT: floating | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ScalarT: ...\ndef var[ScalarT: floating | object_ | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef var[ArrayT: ndarray[tuple[Any, ...], dtype[floating | object_ | timedelta64]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ArrayT: ...\ndef var[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ScalarT: ...\ndef var[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef var[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None,\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef var[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef var[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT,\n ddof : float = 0,\n keepdims : _NoValueType | builtins.bool = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ArrayT: ...\ndef var(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> Any: ...\ndef var(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray: ...\ndef var(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray: ...", "kind": 2, "label": "var", "sortText": "0.9999.var"}, {"detail": "\n@overload\ndef view(self: ndarray, /) -> ndarray: ...\ndef view[DTypeT: dtype](\n self: ndarray,\n /,\n dtype: numpy._HasDType[DTypeT] | DTypeT\n) -> ndarray[tuple[Any, ...], DTypeT]: ...\ndef view[ScalarT: generic](\n self: ndarray,\n /,\n dtype: _DTypeLike\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef view[ArrayT: ndarray](\n self: ndarray,\n /,\n *,\n type: type[ArrayT]\n) -> ArrayT: ...\ndef view[ArrayT: ndarray](\n self: ndarray,\n /,\n dtype: type[ArrayT]\n) -> ArrayT: ...\ndef view(\n self: ndarray,\n /,\n dtype: DTypeLike\n) -> ndarray: ...\ndef view[ArrayT: ndarray](\n self: ndarray,\n /,\n dtype: DTypeLike,\n type: type[ArrayT]\n) -> ArrayT: ...", "kind": 2, "label": "view", "sortText": "0.9999.view"}, {"detail": "\n@overload\ndef __abs__[ShapeT: tuple[int, ...], NBitT: NBitBase](self: ndarray[ShapeT, dtype[complexfloating[NBitT, NBitT]]], /) -> ndarray[ShapeT, dtype[floating[NBitT]]]: ...\ndef __abs__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer | object_ | timedelta64]]](self: ArrayT, /) -> ArrayT: ...", "kind": 2, "label": "__abs__", "sortText": "3.9999.__abs__"}, {"detail": "\n@overload\ndef __add__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __add__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __add__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[datetime64]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[datetime64]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[bytes_]],\n other: _ArrayLikeBytes_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[bytes_]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[str_]],\n other: _ArrayLikeStr_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[str_]]: ...\ndef __add__(\n self: ndarray[Any, StringDType],\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ndarray[tuple[Any, ...], StringDType]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __add__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__add__", "sortText": "3.9999.__add__"}, {"detail": "\n@overload\ndef __and__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __and__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __and__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __and__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __and__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__and__", "sortText": "3.9999.__and__"}, {"detail": "\n@overload\ndef __array__(\n self : ndarray,\n dtype: None = None,\n /,\n *,\n copy : bool | None = None\n) -> ndarray: ...\ndef __array__[DTypeT: dtype](\n self : ndarray,\n dtype: DTypeT,\n /,\n *,\n copy : bool | None = None\n) -> ndarray[tuple[Any, ...], DTypeT]: ...", "kind": 2, "label": "__array__", "sortText": "3.9999.__array__"}, {"detail": "def __array_finalize__(\n self: ndarray,\n obj: ndarray | None,\n /\n) -> None: ...", "kind": 2, "label": "__array_finalize__", "sortText": "3.9999.__array_finalize__"}, {"detail": "def __array_function__(\n self: ndarray,\n func: (...) -> Any,\n types: Iterable[type[Any]],\n args: Iterable[Any],\n kwargs: Mapping[str, Any]\n) -> Any: ...", "kind": 2, "label": "__array_function__", "sortText": "3.9999.__array_function__"}, {"detail": "dict[str, Any]", "kind": 5, "label": "__array_interface__", "sortText": "3.9999.__array_interface__"}, {"detail": "def __array_namespace__(\n self : ndarray,\n /,\n *,\n api_version: Literal['2021.12', '2022.12', '2023.12', '2024.12', '2025.12'] | None = None\n) -> ModuleType: ...", "kind": 2, "label": "__array_namespace__", "sortText": "3.9999.__array_namespace__"}, {"detail": "float", "kind": 5, "label": "__array_priority__", "sortText": "3.9999.__array_priority__"}, {"detail": "CapsuleType", "kind": 5, "label": "__array_struct__", "sortText": "3.9999.__array_struct__"}, {"detail": "def __array_ufunc__(\n self: ndarray,\n ufunc: ufunc,\n method: Literal['__call__', 'accumulate', 'at', 'outer', 'reduce', 'reduceat'],\n /,\n *inputs: Any,\n **kwargs: Any\n) -> Any: ...", "kind": 2, "label": "__array_ufunc__", "sortText": "3.9999.__array_ufunc__"}, {"detail": "def __array_wrap__[ShapeT: tuple[int, ...], DTypeT: dtype](\n self : ndarray,\n array : ndarray[ShapeT, DTypeT],\n context : tuple[ufunc, tuple[Any, ...], int] | None = ...,\n return_scalar: bool = ...,\n /\n) -> ndarray[ShapeT, DTypeT]: ...", "kind": 2, "label": "__array_wrap__", "sortText": "3.9999.__array_wrap__"}, {"detail": "def __bool__(self: ndarray, /) -> bool: ...", "kind": 2, "label": "__bool__", "sortText": "3.9999.__bool__"}, {"detail": "def __buffer__(\n self: ndarray,\n flags: int,\n /\n) -> memoryview: ...", "kind": 2, "label": "__buffer__", "sortText": "3.9999.__buffer__"}, {"detail": "def __class_getitem__(\n cls: type[ndarray],\n item: Any,\n /\n) -> GenericAlias: ...", "kind": 2, "label": "__class_getitem__", "sortText": "3.9999.__class_getitem__"}, {"detail": "def __complex__(self: ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]], /) -> complex: ...", "kind": 2, "label": "__complex__", "sortText": "3.9999.__complex__"}, {"detail": "def __contains__(\n self: ndarray,\n value: object,\n /\n) -> bool: ...", "kind": 2, "label": "__contains__", "sortText": "3.9999.__contains__"}, {"detail": "def __copy__(self: ndarray) -> ndarray: ...", "kind": 2, "label": "__copy__", "sortText": "3.9999.__copy__"}, {"detail": "def __deepcopy__(\n self: ndarray,\n memo: dict[int, Any] | None,\n /\n) -> ndarray: ...", "kind": 2, "label": "__deepcopy__", "sortText": "3.9999.__deepcopy__"}, {"detail": "\n@overload\ndef __divmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n rhs: numpy.bool | int,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[ScalarT]], ndarray[tuple[Any, ...], dtype[ScalarT]]]: ...\ndef __divmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n rhs: _ArrayLikeBool_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[ScalarT]], ndarray[tuple[Any, ...], dtype[ScalarT]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n rhs: _ArrayLikeBool_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]]: ...\ndef __divmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n rhs: _ArrayLike,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[ScalarT]], ndarray[tuple[Any, ...], dtype[ScalarT]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n rhs: _ArrayLikeFloat64_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n rhs: _ArrayLike,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n rhs: _ArrayLikeUInt_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[unsignedinteger]], ndarray[tuple[Any, ...], dtype[unsignedinteger]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n rhs: _ArrayLikeInt_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[signedinteger]], ndarray[tuple[Any, ...], dtype[signedinteger]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n rhs: _ArrayLikeFloat_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[floating]], ndarray[tuple[Any, ...], dtype[floating]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n rhs: _ArrayLike,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[signedinteger[_64Bit]]], ndarray[tuple[Any, ...], dtype[timedelta64]]]: ...", "kind": 2, "label": "__divmod__", "sortText": "3.9999.__divmod__"}, {"detail": "def __dlpack__(\n self : ndarray[tuple[Any, ...], dtype[number]],\n /,\n *,\n stream : int | Any | None = None,\n max_version: tuple[int, int] | None = None,\n dl_device : tuple[int, int] | None = None,\n copy : bool | None = None\n) -> CapsuleType: ...", "kind": 2, "label": "__dlpack__", "sortText": "3.9999.__dlpack__"}, {"detail": "def __dlpack_device__(self: ndarray, /) -> tuple[Literal[1], Literal[0]]: ...", "kind": 2, "label": "__dlpack_device__", "sortText": "3.9999.__dlpack_device__"}, {"detail": "def __eq__(\n self: ndarray,\n other: Any,\n /\n) -> Any: ...", "kind": 2, "label": "__eq__", "sortText": "3.9999.__eq__"}, {"detail": "def __float__(self: ndarray, /) -> float: ...", "kind": 2, "label": "__float__", "sortText": "3.9999.__float__"}, {"detail": "\n@overload\ndef __floordiv__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __floordiv__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __floordiv__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_64Bit]]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLikeBool_co,\n /\n) -> NoReturn: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __floordiv__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__floordiv__", "sortText": "3.9999.__floordiv__"}, {"detail": "\n@overload\ndef __ge__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ge__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ge__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ge__(\n self: ndarray[tuple[Any, ...], dtype[bytes_]],\n other: _ArrayLikeBytes_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ge__(\n self: ndarray[tuple[Any, ...], StringDType | dtype[str_]],\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ge__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: object,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ge__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...", "kind": 2, "label": "__ge__", "sortText": "3.9999.__ge__"}, {"detail": "\n@overload\ndef __getitem__(\n self: ndarray,\n key: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]] | tuple[ndarray[tuple[Any, ...], dtype[numpy.bool | integer]], ...],\n /\n) -> ndarray: ...\ndef __getitem__(\n self: ndarray,\n key: SupportsIndex | tuple[SupportsIndex, ...],\n /\n) -> Any: ...\ndef __getitem__(\n self: ndarray,\n key: _ToIndices,\n /\n) -> ndarray: ...\ndef __getitem__(\n self: ndarray[tuple[Any, ...], dtype[void]],\n key: str,\n /\n) -> ndarray: ...\ndef __getitem__(\n self: ndarray[tuple[Any, ...], dtype[void]],\n key: list[str],\n /\n) -> ndarray[tuple[Any, ...], dtype[void]]: ...", "kind": 2, "label": "__getitem__", "sortText": "3.9999.__getitem__"}, {"detail": "\n@overload\ndef __gt__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __gt__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __gt__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __gt__(\n self: ndarray[tuple[Any, ...], dtype[bytes_]],\n other: _ArrayLikeBytes_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __gt__(\n self: ndarray[tuple[Any, ...], StringDType | dtype[str_]],\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __gt__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: object,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __gt__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...", "kind": 2, "label": "__gt__", "sortText": "3.9999.__gt__"}, {"detail": "None", "kind": 5, "label": "__hash__", "sortText": "3.9999.__hash__"}, {"detail": "\n@overload\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool]]](\n self: ArrayT,\n other: _ArrayLikeBool_co,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[complexfloating]]](\n self: ArrayT,\n other: _ArrayLikeComplex_co,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[inexact]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[number]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[datetime64 | timedelta64]]](\n self: ArrayT,\n other: _ArrayLikeTD64_co,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[bytes_]]](\n self: ArrayT,\n other: _ArrayLikeBytes_co,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], StringDType | dtype[str_]]](\n self: ArrayT,\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__iadd__", "sortText": "3.9999.__iadd__"}, {"detail": "\n@overload\ndef __iand__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool]]](\n self: ArrayT,\n other: _ArrayLikeBool_co,\n /\n) -> ArrayT: ...\ndef __iand__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __iand__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__iand__", "sortText": "3.9999.__iand__"}, {"detail": "\n@overload\ndef __ifloordiv__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __ifloordiv__[ArrayT: ndarray[tuple[Any, ...], dtype[floating | timedelta64]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __ifloordiv__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__ifloordiv__", "sortText": "3.9999.__ifloordiv__"}, {"detail": "\n@overload\ndef __ilshift__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __ilshift__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__ilshift__", "sortText": "3.9999.__ilshift__"}, {"detail": "\n@overload\ndef __imatmul__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool]]](\n self: ArrayT,\n other: _ArrayLikeBool_co,\n /\n) -> ArrayT: ...\ndef __imatmul__[ArrayT: ndarray[tuple[Any, ...], dtype[complexfloating]]](\n self: ArrayT,\n other: _ArrayLikeComplex_co,\n /\n) -> ArrayT: ...\ndef __imatmul__[ArrayT: ndarray[tuple[Any, ...], dtype[inexact]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __imatmul__[ArrayT: ndarray[tuple[Any, ...], dtype[number]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __imatmul__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__imatmul__", "sortText": "3.9999.__imatmul__"}, {"detail": "\n@overload\ndef __imod__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __imod__[ArrayT: ndarray[tuple[Any, ...], dtype[floating]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __imod__[ArrayT: ndarray[tuple[Any, ...], dtype[timedelta64]]](\n self: ArrayT,\n other: _ArrayLike,\n /\n) -> ArrayT: ...\ndef __imod__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__imod__", "sortText": "3.9999.__imod__"}, {"detail": "\n@overload\ndef __imul__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool]]](\n self: ArrayT,\n other: _ArrayLikeBool_co,\n /\n) -> ArrayT: ...\ndef __imul__[ArrayT: ndarray[tuple[Any, ...], dtype[complexfloating]]](\n self: ArrayT,\n other: _ArrayLikeComplex_co,\n /\n) -> ArrayT: ...\ndef __imul__[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | timedelta64]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __imul__[ArrayT: ndarray[tuple[Any, ...], dtype[character | number]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __imul__[ArrayT: ndarray[tuple[Any, ...], StringDType | dtype[str_]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __imul__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__imul__", "sortText": "3.9999.__imul__"}, {"detail": "def __index__(self: ndarray[tuple[Any, ...], dtype[integer]], /) -> int: ...", "kind": 2, "label": "__index__", "sortText": "3.9999.__index__"}, {"detail": "def __int__(self: ndarray, /) -> int: ...", "kind": 2, "label": "__int__", "sortText": "3.9999.__int__"}, {"detail": "def __invert__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | object_]]](self: ArrayT, /) -> ArrayT: ...", "kind": 2, "label": "__invert__", "sortText": "3.9999.__invert__"}, {"detail": "\n@overload\ndef __ior__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool]]](\n self: ArrayT,\n other: _ArrayLikeBool_co,\n /\n) -> ArrayT: ...\ndef __ior__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __ior__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__ior__", "sortText": "3.9999.__ior__"}, {"detail": "\n@overload\ndef __ipow__[ArrayT: ndarray[tuple[Any, ...], dtype[complexfloating]]](\n self: ArrayT,\n other: _ArrayLikeComplex_co,\n /\n) -> ArrayT: ...\ndef __ipow__[ArrayT: ndarray[tuple[Any, ...], dtype[inexact]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __ipow__[ArrayT: ndarray[tuple[Any, ...], dtype[number]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __ipow__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__ipow__", "sortText": "3.9999.__ipow__"}, {"detail": "\n@overload\ndef __irshift__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __irshift__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__irshift__", "sortText": "3.9999.__irshift__"}, {"detail": "\n@overload\ndef __isub__[ArrayT: ndarray[tuple[Any, ...], dtype[complexfloating]]](\n self: ArrayT,\n other: _ArrayLikeComplex_co,\n /\n) -> ArrayT: ...\ndef __isub__[ArrayT: ndarray[tuple[Any, ...], dtype[inexact]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __isub__[ArrayT: ndarray[tuple[Any, ...], dtype[number]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __isub__[ArrayT: ndarray[tuple[Any, ...], dtype[datetime64 | timedelta64]]](\n self: ArrayT,\n other: _ArrayLikeTD64_co,\n /\n) -> ArrayT: ...\ndef __isub__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__isub__", "sortText": "3.9999.__isub__"}, {"detail": "\n@overload\ndef __iter__(self: ndarray[tuple[Never, Never, Never, Never, Never, Never, Never, Never, Never], Any], /) -> Iterator[Any]: ...\ndef __iter__[ScalarT: _ScalarNotObject](self: ndarray[tuple[int], dtype[ScalarT]], /) -> Iterator[ScalarT]: ...\ndef __iter__(self: ndarray[tuple[int], StringDType], /) -> Iterator[str]: ...\ndef __iter__[DTypeT: dtype](self: ndarray[tuple[int, int, *tuple[int, ...]], DTypeT], /) -> Iterator[ndarray[tuple[Any, ...], DTypeT]]: ...\ndef __iter__(self: ndarray, /) -> Iterator[Any]: ...", "kind": 2, "label": "__iter__", "sortText": "3.9999.__iter__"}, {"detail": "\n@overload\ndef __itruediv__[ArrayT: ndarray[tuple[Any, ...], dtype[complexfloating]]](\n self: ArrayT,\n other: _ArrayLikeComplex_co,\n /\n) -> ArrayT: ...\ndef __itruediv__[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | timedelta64]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __itruediv__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__itruediv__", "sortText": "3.9999.__itruediv__"}, {"detail": "\n@overload\ndef __ixor__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool]]](\n self: ArrayT,\n other: _ArrayLikeBool_co,\n /\n) -> ArrayT: ...\ndef __ixor__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __ixor__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__ixor__", "sortText": "3.9999.__ixor__"}, {"detail": "\n@overload\ndef __le__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __le__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __le__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __le__(\n self: ndarray[tuple[Any, ...], dtype[bytes_]],\n other: _ArrayLikeBytes_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __le__(\n self: ndarray[tuple[Any, ...], StringDType | dtype[str_]],\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __le__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: object,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __le__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...", "kind": 2, "label": "__le__", "sortText": "3.9999.__le__"}, {"detail": "def __len__(self: ndarray) -> int: ...", "kind": 2, "label": "__len__", "sortText": "3.9999.__len__"}, {"detail": "\n@overload\ndef __lshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __lshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __lshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __lshift__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __lshift__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__lshift__", "sortText": "3.9999.__lshift__"}, {"detail": "\n@overload\ndef __lt__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __lt__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __lt__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __lt__(\n self: ndarray[tuple[Any, ...], dtype[bytes_]],\n other: _ArrayLikeBytes_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __lt__(\n self: ndarray[tuple[Any, ...], StringDType | dtype[str_]],\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __lt__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: object,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __lt__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...", "kind": 2, "label": "__lt__", "sortText": "3.9999.__lt__"}, {"detail": "\n@overload\ndef __matmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __matmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[floating[_64Bit]]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[complexfloating[_64Bit, _64Bit]]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __matmul__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__matmul__", "sortText": "3.9999.__matmul__"}, {"detail": "\n@overload\ndef __mod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __mod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __mod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __mod__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__mod__", "sortText": "3.9999.__mod__"}, {"detail": "\n@overload\ndef __mul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __mul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __mul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __mul__(\n self: ndarray[Any, StringDType | dtype[character]],\n other: _ArrayLikeInt,\n /\n) -> ndarray: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __mul__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__mul__", "sortText": "3.9999.__mul__"}, {"detail": "def __ne__(\n self: ndarray,\n other: Any,\n /\n) -> Any: ...", "kind": 2, "label": "__ne__", "sortText": "3.9999.__ne__"}, {"detail": "def __neg__[ArrayT: ndarray[tuple[Any, ...], dtype[number | object_ | timedelta64]]](self: ArrayT, /) -> ArrayT: ...", "kind": 2, "label": "__neg__", "sortText": "3.9999.__neg__"}, {"detail": "def __new__[Self@ndarray: ndarray](\n cls : type[Self@ndarray],\n shape : _ShapeLike,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = ...,\n buffer : Buffer | None = ...,\n offset : SupportsIndex = ...,\n strides: Sequence[SupportsIndex] | SupportsIndex | None = ...,\n order : _OrderKACF = ...\n) -> Self@ndarray: ...", "kind": 5, "label": "__new__", "sortText": "3.9999.__new__"}, {"detail": "\n@overload\ndef __or__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __or__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __or__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __or__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __or__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__or__", "sortText": "3.9999.__or__"}, {"detail": "def __pos__[ArrayT: ndarray[tuple[Any, ...], dtype[number | object_ | timedelta64]]](self: ArrayT, /) -> ArrayT: ...", "kind": 2, "label": "__pos__", "sortText": "3.9999.__pos__"}, {"detail": "\n@overload\ndef __pow__[ScalarT: number](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __pow__[ScalarT: number](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __pow__[ScalarT: number](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n mod : None = None,\n /\n) -> Any: ...\ndef __pow__(\n self : ndarray,\n other: _ArrayLikeObject_co,\n mod : None = None,\n /\n) -> Any: ...", "kind": 2, "label": "__pow__", "sortText": "3.9999.__pow__"}, {"detail": "\n@overload\ndef __radd__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __radd__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __radd__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[datetime64]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[datetime64]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[bytes_]],\n other: _ArrayLikeBytes_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[bytes_]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[str_]],\n other: _ArrayLikeStr_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[str_]]: ...\ndef __radd__(\n self: ndarray[Any, StringDType],\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ndarray[tuple[Any, ...], StringDType]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __radd__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__radd__", "sortText": "3.9999.__radd__"}, {"detail": "\n@overload\ndef __rand__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __rand__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rand__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rand__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rand__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rand__", "sortText": "3.9999.__rand__"}, {"detail": "\n@overload\ndef __rdivmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n lhs: numpy.bool | int,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[ScalarT]], ndarray[tuple[Any, ...], dtype[ScalarT]]]: ...\ndef __rdivmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n lhs: _ArrayLikeBool_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[ScalarT]], ndarray[tuple[Any, ...], dtype[ScalarT]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n lhs: _ArrayLikeBool_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]]: ...\ndef __rdivmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n lhs: _ArrayLike,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[ScalarT]], ndarray[tuple[Any, ...], dtype[ScalarT]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n lhs: _ArrayLikeFloat64_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n lhs: _ArrayLike,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n lhs: _ArrayLikeUInt_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[unsignedinteger]], ndarray[tuple[Any, ...], dtype[unsignedinteger]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n lhs: _ArrayLikeInt_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[signedinteger]], ndarray[tuple[Any, ...], dtype[signedinteger]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n lhs: _ArrayLikeFloat_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[floating]], ndarray[tuple[Any, ...], dtype[floating]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n lhs: _ArrayLike,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[signedinteger[_64Bit]]], ndarray[tuple[Any, ...], dtype[timedelta64]]]: ...", "kind": 2, "label": "__rdivmod__", "sortText": "3.9999.__rdivmod__"}, {"detail": "\n@overload\ndef __rfloordiv__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rfloordiv__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __rfloordiv__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_64Bit]]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[floating | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rfloordiv__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rfloordiv__", "sortText": "3.9999.__rfloordiv__"}, {"detail": "\n@overload\ndef __rlshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __rlshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rlshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rlshift__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rlshift__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rlshift__", "sortText": "3.9999.__rlshift__"}, {"detail": "\n@overload\ndef __rmatmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __rmatmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[floating[_64Bit]]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[complexfloating[_64Bit, _64Bit]]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rmatmul__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rmatmul__", "sortText": "3.9999.__rmatmul__"}, {"detail": "\n@overload\ndef __rmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __rmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rmod__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rmod__", "sortText": "3.9999.__rmod__"}, {"detail": "\n@overload\ndef __rmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __rmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rmul__(\n self: ndarray[Any, StringDType | dtype[character]],\n other: _ArrayLikeInt,\n /\n) -> ndarray: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rmul__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rmul__", "sortText": "3.9999.__rmul__"}, {"detail": "\n@overload\ndef __ror__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ror__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __ror__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __ror__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __ror__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__ror__", "sortText": "3.9999.__ror__"}, {"detail": "\n@overload\ndef __rpow__[ScalarT: number](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rpow__[ScalarT: number](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __rpow__[ScalarT: number](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n mod : None = None,\n /\n) -> Any: ...\ndef __rpow__(\n self : ndarray,\n other: _ArrayLikeObject_co,\n mod : None = None,\n /\n) -> Any: ...", "kind": 2, "label": "__rpow__", "sortText": "3.9999.__rpow__"}, {"detail": "\n@overload\ndef __rrshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __rrshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rrshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rrshift__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rrshift__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rrshift__", "sortText": "3.9999.__rrshift__"}, {"detail": "\n@overload\ndef __rshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __rshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rshift__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rshift__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rshift__", "sortText": "3.9999.__rshift__"}, {"detail": "\n@overload\ndef __rsub__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rsub__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> NoReturn: ...\ndef __rsub__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[datetime64]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rsub__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rsub__", "sortText": "3.9999.__rsub__"}, {"detail": "\n@overload\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]] | ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _NestedSequence[_SupportsArray[dtype[numpy.bool | integer]]] | _NestedSequence[_SupportsArray[dtype[floating[_64Bit]]]] | _NestedSequence[int] | _SupportsArray[dtype[numpy.bool | integer]] | _SupportsArray[dtype[floating[_64Bit]]] | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[floating]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[complexfloating]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[inexact]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[inexact]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[floating | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rtruediv__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rtruediv__", "sortText": "3.9999.__rtruediv__"}, {"detail": "\n@overload\ndef __rxor__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __rxor__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rxor__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rxor__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rxor__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rxor__", "sortText": "3.9999.__rxor__"}, {"detail": "\n@overload\ndef __setitem__(\n self: ndarray[Any, StringDType | dtype[numpy.bool | flexible | object_]],\n key: _ToIndices,\n value: object,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray[tuple[Any, ...], dtype[integer]],\n key: _ToIndices,\n value: SupportsIndex | SupportsInt | _NestedSequence[_ConvertibleToInt] | _NestedSequence[_SupportsArray[dtype[numpy.bool | integer]]] | _NestedSequence[int] | _SupportsArray[dtype[numpy.bool | integer]] | bytes | int | str,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray[tuple[Any, ...], dtype[floating]],\n key: _ToIndices,\n value: SupportsFloat | SupportsIndex | _NestedSequence[SupportsFloat | SupportsIndex | bytes | str | None] | _NestedSequence[_SupportsArray[dtype[numpy.bool | floating | integer]]] | _NestedSequence[float] | _SupportsArray[dtype[numpy.bool | floating | integer]] | bytes | float | str | None,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray[tuple[Any, ...], dtype[complexfloating]],\n key: _ToIndices,\n value: SupportsComplex | SupportsFloat | SupportsIndex | _NestedSequence[SupportsComplex | SupportsFloat | SupportsIndex | bytes | str | None] | _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _SupportsArray[dtype[numpy.bool | number]] | bytes | complex | str | None,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n key: _ToIndices,\n value: _NestedSequence[_ConvertibleToTD64] | numpy.bool | bytes | character | int | number | str | timedelta | timedelta64 | None,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n key: _ToIndices,\n value: _NestedSequence[_ConvertibleToDT64] | numpy.bool | bytes | character | date | datetime64 | int | number | str | None,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray[tuple[Any, ...], dtype[void]],\n key: list[str] | str,\n value: object,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray,\n key: _ToIndices,\n value: ArrayLike,\n /\n) -> None: ...", "kind": 2, "label": "__setitem__", "sortText": "3.9999.__setitem__"}, {"detail": "def __setstate__[_DTypeT_co: dtype = dtype](\n self: ndarray,\n state: tuple[SupportsIndex, _ShapeLike, _DTypeT_co, numpy.bool, bytes | list[Any]],\n /\n) -> None: ...", "kind": 2, "label": "__setstate__", "sortText": "3.9999.__setstate__"}, {"detail": "\n@overload\ndef __sub__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __sub__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> NoReturn: ...\ndef __sub__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[datetime64]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __sub__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__sub__", "sortText": "3.9999.__sub__"}, {"detail": "\n@overload\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]] | ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _NestedSequence[_SupportsArray[dtype[numpy.bool | integer]]] | _NestedSequence[_SupportsArray[dtype[floating[_64Bit]]]] | _NestedSequence[int] | _SupportsArray[dtype[numpy.bool | integer]] | _SupportsArray[dtype[floating[_64Bit]]] | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[floating]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[complexfloating]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[inexact]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[inexact]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLikeBool_co,\n /\n) -> NoReturn: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __truediv__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__truediv__", "sortText": "3.9999.__truediv__"}, {"detail": "\n@overload\ndef __xor__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __xor__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __xor__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __xor__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __xor__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__xor__", "sortText": "3.9999.__xor__"}]}} {"suite": "data_science", "label": "edit array then complete (edit+completion)", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 10, "character": 31, "iteration": 4, "result": {"isIncomplete": false, "items": [{"detail": "ndarray", "kind": 5, "label": "T", "sortText": "0.9999.T"}, {"detail": "\n@overload\ndef all(\n self : ndarray,\n axis : None = None,\n out : None = None,\n keepdims: Literal[False] = False,\n *,\n where : _ArrayLikeBool_co = True\n) -> numpy.bool: ...\ndef all(\n self : ndarray,\n axis : int | tuple[int, ...],\n out : None = None,\n keepdims: Literal[False] = False,\n *,\n where : _ArrayLikeBool_co = True\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef all(\n self : ndarray,\n axis : int | tuple[int, ...] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n where : _ArrayLikeBool_co = True\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef all[ArrayT: ndarray](\n self : ndarray,\n axis : int | tuple[int, ...] | None = None,\n *,\n out : ArrayT,\n keepdims: builtins.bool = False,\n where : _ArrayLikeBool_co = True\n) -> ArrayT: ...\ndef all[ArrayT: ndarray](\n self : ndarray,\n axis : int | tuple[int, ...] | None,\n out : ArrayT,\n keepdims: builtins.bool = False,\n *,\n where : _ArrayLikeBool_co = True\n) -> ArrayT: ...", "kind": 2, "label": "all", "sortText": "0.9999.all"}, {"detail": "\n@overload\ndef any(\n self : ndarray,\n axis : None = None,\n out : None = None,\n keepdims: Literal[False] = False,\n *,\n where : _ArrayLikeBool_co = True\n) -> numpy.bool: ...\ndef any(\n self : ndarray,\n axis : int | tuple[int, ...],\n out : None = None,\n keepdims: Literal[False] = False,\n *,\n where : _ArrayLikeBool_co = True\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef any(\n self : ndarray,\n axis : int | tuple[int, ...] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n where : _ArrayLikeBool_co = True\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef any[ArrayT: ndarray](\n self : ndarray,\n axis : int | tuple[int, ...] | None = None,\n *,\n out : ArrayT,\n keepdims: builtins.bool = False,\n where : _ArrayLikeBool_co = True\n) -> ArrayT: ...\ndef any[ArrayT: ndarray](\n self : ndarray,\n axis : int | tuple[int, ...] | None,\n out : ArrayT,\n keepdims: builtins.bool = False,\n *,\n where : _ArrayLikeBool_co = True\n) -> ArrayT: ...", "kind": 2, "label": "any", "sortText": "0.9999.any"}, {"detail": "\n@overload\ndef argmax(\n self : ndarray,\n axis : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] = False\n) -> signedinteger[_NBitIntP]: ...\ndef argmax(\n self : ndarray,\n axis : SupportsIndex,\n out : None = None,\n *,\n keepdims: Literal[False] = False\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef argmax(\n self : ndarray,\n axis : SupportsIndex | None = None,\n out : None = None,\n *,\n keepdims: Literal[True]\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef argmax[ArrayT: ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]](\n self : ndarray,\n axis : SupportsIndex | None = None,\n *,\n out : ArrayT,\n keepdims: bool = False\n) -> ArrayT: ...\ndef argmax[ArrayT: ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]](\n self : ndarray,\n axis : SupportsIndex | None,\n out : ArrayT,\n *,\n keepdims: bool = False\n) -> ArrayT: ...", "kind": 2, "label": "argmax", "sortText": "0.9999.argmax"}, {"detail": "\n@overload\ndef argmin(\n self : ndarray,\n axis : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] = False\n) -> signedinteger[_NBitIntP]: ...\ndef argmin(\n self : ndarray,\n axis : SupportsIndex,\n out : None = None,\n *,\n keepdims: Literal[False] = False\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef argmin(\n self : ndarray,\n axis : SupportsIndex | None = None,\n out : None = None,\n *,\n keepdims: Literal[True]\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef argmin[ArrayT: ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]](\n self : ndarray,\n axis : SupportsIndex | None = None,\n *,\n out : ArrayT,\n keepdims: bool = False\n) -> ArrayT: ...\ndef argmin[ArrayT: ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]](\n self : ndarray,\n axis : SupportsIndex | None,\n out : ArrayT,\n *,\n keepdims: bool = False\n) -> ArrayT: ...", "kind": 2, "label": "argmin", "sortText": "0.9999.argmin"}, {"detail": "\n@overload\ndef argpartition(\n self : ndarray,\n kth : _ArrayLikeInt,\n /,\n axis : None,\n kind : Literal['introselect'] = 'introselect',\n order: None = None\n) -> ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]: ...\ndef argpartition(\n self : ndarray,\n kth : _ArrayLikeInt,\n /,\n axis : SupportsIndex = -1,\n kind : Literal['introselect'] = 'introselect',\n order: None = None\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef argpartition(\n self : ndarray[tuple[Any, ...], dtype[void]],\n kth : _ArrayLikeInt,\n /,\n axis : None,\n kind : Literal['introselect'] = 'introselect',\n order: Sequence[str] | str | None = None\n) -> ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]: ...\ndef argpartition(\n self : ndarray[tuple[Any, ...], dtype[void]],\n kth : _ArrayLikeInt,\n /,\n axis : SupportsIndex = -1,\n kind : Literal['introselect'] = 'introselect',\n order: Sequence[str] | str | None = None\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...", "kind": 2, "label": "argpartition", "sortText": "0.9999.argpartition"}, {"detail": "def argsort(\n self : ndarray,\n axis : SupportsIndex | None = ...,\n kind : Literal['H', 'M', 'Q', 'S', 'heap', 'heapsort', 'merge', 'mergesort', 'quick', 'quicksort', 'stable', 'stablesort'] | None = ...,\n order : Sequence[str] | str | None = ...,\n *,\n stable : bool | None = ...,\n descending: bool | None = ...\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...", "kind": 2, "label": "argsort", "sortText": "0.9999.argsort"}, {"detail": "\n@overload\ndef astype[ScalarT: generic](\n self : ndarray,\n dtype : _DTypeLike,\n order : _OrderKACF = ...,\n casting: _CastingKind = ...,\n subok : bool = ...,\n copy : _CopyMode | bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef astype(\n self : ndarray,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None,\n order : _OrderKACF = ...,\n casting: _CastingKind = ...,\n subok : bool = ...,\n copy : _CopyMode | bool = ...\n) -> ndarray: ...", "kind": 2, "label": "astype", "sortText": "0.9999.astype"}, {"detail": "ndarray | None", "kind": 5, "label": "base", "sortText": "0.9999.base"}, {"detail": "def byteswap(\n self : ndarray,\n inplace: bool = ...\n) -> ndarray: ...", "kind": 2, "label": "byteswap", "sortText": "0.9999.byteswap"}, {"detail": "\n@overload\ndef choose(\n self : ndarray,\n /,\n choices: ArrayLike,\n out : None = None,\n mode : _ModeKind = 'raise'\n) -> ndarray: ...\ndef choose[ArrayT: ndarray](\n self : ndarray,\n /,\n choices: ArrayLike,\n out : ArrayT,\n mode : _ModeKind = 'raise'\n) -> ArrayT: ...", "kind": 2, "label": "choose", "sortText": "0.9999.choose"}, {"detail": "\n@overload\ndef clip(\n self : ndarray,\n /,\n min : ArrayLike,\n max : Buffer | _NestedSequence[bytes | complex | str] | _NestedSequence[_SupportsArray[dtype]] | _SupportsArray[dtype] | bytes | complex | str | None = None,\n out : None = None,\n **kwargs: Any\n) -> ndarray: ...\ndef clip(\n self : ndarray,\n /,\n min : None,\n max : ArrayLike,\n out : None = None,\n **kwargs: Any\n) -> ndarray: ...\ndef clip(\n self : ndarray,\n /,\n min : None = None,\n *,\n max : ArrayLike,\n out : None = None,\n **kwargs: Any\n) -> ndarray: ...\ndef clip[ArrayT: ndarray](\n self: ndarray,\n /,\n min: ArrayLike,\n max: Buffer | _NestedSequence[bytes | complex | str] | _NestedSequence[_SupportsArray[dtype]] | _SupportsArray[dtype] | bytes | complex | str | None,\n out: ArrayT,\n **kwargs: Any\n) -> ArrayT: ...\ndef clip[ArrayT: ndarray](\n self : ndarray,\n /,\n min : ArrayLike,\n max : Buffer | _NestedSequence[bytes | complex | str] | _NestedSequence[_SupportsArray[dtype]] | _SupportsArray[dtype] | bytes | complex | str | None = None,\n *,\n out : ArrayT,\n **kwargs: Any\n) -> ArrayT: ...\ndef clip[ArrayT: ndarray](\n self: ndarray,\n /,\n min: None,\n max: ArrayLike,\n out: ArrayT,\n **kwargs: Any\n) -> ArrayT: ...\ndef clip[ArrayT: ndarray](\n self : ndarray,\n /,\n min : None = None,\n *,\n max : ArrayLike,\n out : ArrayT,\n **kwargs: Any\n) -> ArrayT: ...", "kind": 2, "label": "clip", "sortText": "0.9999.clip"}, {"detail": "\n@overload\ndef compress(\n self : ndarray,\n /,\n condition: _ArrayLikeInt_co,\n axis : SupportsIndex | None = None,\n out : None = None\n) -> ndarray: ...\ndef compress[ArrayT: ndarray](\n self: ndarray,\n /,\n condition: _ArrayLikeInt_co,\n axis: SupportsIndex | None,\n out: ArrayT\n) -> ArrayT: ...\ndef compress[ArrayT: ndarray](\n self : ndarray,\n /,\n condition: _ArrayLikeInt_co,\n axis : SupportsIndex | None = None,\n *,\n out : ArrayT\n) -> ArrayT: ...", "kind": 2, "label": "compress", "sortText": "0.9999.compress"}, {"detail": "def conj(self: ndarray) -> ndarray: ...", "kind": 2, "label": "conj", "sortText": "0.9999.conj"}, {"detail": "def conjugate(self: ndarray) -> ndarray: ...", "kind": 2, "label": "conjugate", "sortText": "0.9999.conjugate"}, {"detail": "def copy(\n self : ndarray,\n order: _OrderKACF = ...\n) -> ndarray: ...", "kind": 2, "label": "copy", "sortText": "0.9999.copy"}, {"detail": "_ctypes[int]", "kind": 5, "label": "ctypes", "sortText": "0.9999.ctypes"}, {"detail": "\n@overload\ndef cumprod[DTypeT: dtype[inexact | object_]](\n self : ndarray[Any, DTypeT],\n axis : None = None,\n dtype: None = None,\n out : None = None\n) -> ndarray[tuple[int], DTypeT]: ...\ndef cumprod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : None = None,\n dtype: None = None,\n out : None = None\n) -> ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]: ...\ndef cumprod[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : None = None,\n *,\n dtype: _DTypeLike,\n out : None = None\n) -> ndarray[tuple[int], dtype[ScalarT]]: ...\ndef cumprod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : None = None,\n *,\n dtype: DTypeLike,\n out : None = None\n) -> ndarray[tuple[int]]: ...\ndef cumprod[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : None,\n dtype: _DTypeLike,\n out : None = None\n) -> ndarray[tuple[int], dtype[ScalarT]]: ...\ndef cumprod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : None,\n dtype: DTypeLike,\n out : None = None\n) -> ndarray[tuple[int]]: ...\ndef cumprod[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | object_]]](\n self : ArrayT,\n axis : SupportsIndex,\n dtype: None = None,\n out : None = None\n) -> ArrayT: ...\ndef cumprod[ShapeT: tuple[int, ...]](\n self : ndarray[ShapeT, dtype[numpy.bool | integer]],\n axis : SupportsIndex,\n dtype: None = None,\n out : None = None\n) -> ndarray[ShapeT, dtype[signedinteger[_NBitIntP]]]: ...\ndef cumprod[ShapeT: tuple[int, ...], ScalarT: generic](\n self : ndarray[ShapeT, dtype[numpy.bool | number | object_]],\n axis : SupportsIndex,\n dtype: _DTypeLike,\n out : None = None\n) -> ndarray[ShapeT, dtype[ScalarT]]: ...\ndef cumprod[ShapeT: tuple[int, ...]](\n self : ndarray[ShapeT, dtype[numpy.bool | number | object_]],\n axis : SupportsIndex,\n dtype: DTypeLike,\n out : None = None\n) -> ndarray[ShapeT]: ...\ndef cumprod[ArrayT: ndarray](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis: SupportsIndex | None,\n dtype: _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None,\n out: ArrayT\n) -> ArrayT: ...\ndef cumprod[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : SupportsIndex | None = None,\n dtype: _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT\n) -> ArrayT: ...", "kind": 2, "label": "cumprod", "sortText": "0.9999.cumprod"}, {"detail": "\n@overload\ndef cumsum[DTypeT: dtype[inexact | object_ | timedelta64]](\n self : ndarray[Any, DTypeT],\n axis : None = None,\n dtype: None = None,\n out : None = None\n) -> ndarray[tuple[int], DTypeT]: ...\ndef cumsum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : None = None,\n dtype: None = None,\n out : None = None\n) -> ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]: ...\ndef cumsum[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n *,\n dtype: _DTypeLike,\n out : None = None\n) -> ndarray[tuple[int], dtype[ScalarT]]: ...\ndef cumsum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n *,\n dtype: DTypeLike,\n out : None = None\n) -> ndarray[tuple[int]]: ...\ndef cumsum[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None,\n dtype: _DTypeLike,\n out : None = None\n) -> ndarray[tuple[int], dtype[ScalarT]]: ...\ndef cumsum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None,\n dtype: DTypeLike,\n out : None = None\n) -> ndarray[tuple[int]]: ...\ndef cumsum[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | object_ | timedelta64]]](\n self : ArrayT,\n axis : SupportsIndex,\n dtype: None = None,\n out : None = None\n) -> ArrayT: ...\ndef cumsum[ShapeT: tuple[int, ...]](\n self : ndarray[ShapeT, dtype[numpy.bool | integer]],\n axis : SupportsIndex,\n dtype: None = None,\n out : None = None\n) -> ndarray[ShapeT, dtype[signedinteger[_NBitIntP]]]: ...\ndef cumsum[ShapeT: tuple[int, ...], ScalarT: generic](\n self : ndarray[ShapeT, dtype[numpy.bool | number | object_ | timedelta64]],\n axis : SupportsIndex,\n dtype: _DTypeLike,\n out : None = None\n) -> ndarray[ShapeT, dtype[ScalarT]]: ...\ndef cumsum[ShapeT: tuple[int, ...]](\n self : ndarray[ShapeT, dtype[numpy.bool | number | object_ | timedelta64]],\n axis : SupportsIndex,\n dtype: DTypeLike,\n out : None = None\n) -> ndarray[ShapeT]: ...\ndef cumsum[ArrayT: ndarray](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis: SupportsIndex | None,\n dtype: _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None,\n out: ArrayT\n) -> ArrayT: ...\ndef cumsum[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : SupportsIndex | None = None,\n dtype: _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT\n) -> ArrayT: ...", "kind": 2, "label": "cumsum", "sortText": "0.9999.cumsum"}, {"detail": "memoryview", "kind": 5, "label": "data", "sortText": "0.9999.data"}, {"detail": "Literal['cpu']", "kind": 5, "label": "device", "sortText": "0.9999.device"}, {"detail": "\n@overload\ndef diagonal[DTypeT: dtype](\n self : ndarray[tuple[Never, Never, Never, Never], DTypeT],\n offset: SupportsIndex = 0,\n axis1 : SupportsIndex = 0,\n axis2 : SupportsIndex = 1\n) -> ndarray[tuple[Any, ...], DTypeT]: ...\ndef diagonal[DTypeT: dtype](\n self : ndarray[tuple[int, int], DTypeT],\n offset: SupportsIndex = 0,\n axis1 : SupportsIndex = 0,\n axis2 : SupportsIndex = 1\n) -> ndarray[tuple[int], DTypeT]: ...\ndef diagonal[DTypeT: dtype](\n self : ndarray[tuple[int, int, int], DTypeT],\n offset: SupportsIndex = 0,\n axis1 : SupportsIndex = 0,\n axis2 : SupportsIndex = 1\n) -> ndarray[tuple[int, int], DTypeT]: ...\ndef diagonal(\n self : ndarray,\n offset: SupportsIndex = 0,\n axis1 : SupportsIndex = 0,\n axis2 : SupportsIndex = 1\n) -> ndarray: ...", "kind": 2, "label": "diagonal", "sortText": "0.9999.diagonal"}, {"detail": "\n@overload\ndef dot(\n self: ndarray,\n b : _ScalarLike_co,\n /,\n out : None = None\n) -> ndarray: ...\ndef dot(\n self: ndarray,\n b : ArrayLike,\n /,\n out : None = None\n) -> Any: ...\ndef dot[ArrayT: ndarray](\n self: ndarray,\n b: ArrayLike,\n /,\n out: ArrayT\n) -> ArrayT: ...", "kind": 2, "label": "dot", "sortText": "0.9999.dot"}, {"detail": "dtype", "kind": 5, "label": "dtype", "sortText": "0.9999.dtype"}, {"detail": "def dump(\n self: ndarray,\n file: PathLike[bytes] | PathLike[str] | SupportsWrite[bytes] | bytes | str\n) -> None: ...", "kind": 2, "label": "dump", "sortText": "0.9999.dump"}, {"detail": "def dumps(self: ndarray) -> bytes: ...", "kind": 2, "label": "dumps", "sortText": "0.9999.dumps"}, {"detail": "def fill(\n self: ndarray,\n /,\n value: Any\n) -> None: ...", "kind": 2, "label": "fill", "sortText": "0.9999.fill"}, {"detail": "flagsobj", "kind": 5, "label": "flags", "sortText": "0.9999.flags"}, {"detail": "flatiter", "kind": 5, "label": "flat", "sortText": "0.9999.flat"}, {"detail": "def flatten(\n self : ndarray,\n /,\n order: _OrderKACF = 'C'\n) -> ndarray[tuple[int]]: ...", "kind": 2, "label": "flatten", "sortText": "0.9999.flatten"}, {"detail": "\n@overload\ndef getfield[ScalarT: generic](\n self : ndarray,\n /,\n dtype : _DTypeLike,\n offset: SupportsIndex = 0\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef getfield(\n self : ndarray,\n /,\n dtype : DTypeLike,\n offset: SupportsIndex = 0\n) -> ndarray: ...", "kind": 2, "label": "getfield", "sortText": "0.9999.getfield"}, {"detail": "ndarray", "kind": 5, "label": "imag", "sortText": "0.9999.imag"}, {"detail": "\n@overload\ndef item[T](\n self : ndarray[tuple[Any, ...], dtype[generic[T]]],\n i0 : SupportsIndex | tuple[SupportsIndex, ...] = ...,\n /,\n *args: SupportsIndex\n) -> T: ...\ndef item(\n self : ndarray[Any, StringDType],\n arg0 : SupportsIndex | tuple[SupportsIndex, ...] = ...,\n /,\n *args: SupportsIndex\n) -> str: ...", "kind": 2, "label": "item", "sortText": "0.9999.item"}, {"detail": "int", "kind": 5, "label": "itemsize", "sortText": "0.9999.itemsize"}, {"detail": "ndarray", "kind": 5, "label": "mT", "sortText": "0.9999.mT"}, {"detail": "\n@overload\ndef max[ScalarT: numpy.bool | datetime64 | number | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef max[ScalarT: numpy.bool | datetime64 | number | object_ | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef max[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool | datetime64 | number | object_ | timedelta64]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef max(\n self : ndarray[tuple[Any, ...], dtype[object_]],\n axis : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef max[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | datetime64 | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n *,\n out : ArrayT,\n keepdims: _NoValueType | builtins.bool = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...", "kind": 2, "label": "max", "sortText": "0.9999.max"}, {"detail": "\n@overload\ndef mean(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer | object_]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> float64: ...\ndef mean(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef mean(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef mean[ScalarT: inexact | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef mean[ScalarT: inexact | object_ | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef mean[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | object_ | timedelta64]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef mean[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef mean[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n keepdims: Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef mean[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None,\n dtype : _DTypeLike,\n out : None = None,\n *,\n keepdims: Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef mean[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _DTypeLike,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef mean[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT,\n keepdims: _NoValueType | builtins.bool = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef mean(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef mean(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray: ...\ndef mean(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray: ...", "kind": 2, "label": "mean", "sortText": "0.9999.mean"}, {"detail": "\n@overload\ndef min[ScalarT: numpy.bool | datetime64 | number | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef min[ScalarT: numpy.bool | datetime64 | number | object_ | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef min[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool | datetime64 | number | object_ | timedelta64]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef min(\n self : ndarray[tuple[Any, ...], dtype[object_]],\n axis : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef min[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | datetime64 | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n *,\n out : ArrayT,\n keepdims: _NoValueType | builtins.bool = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...", "kind": 2, "label": "min", "sortText": "0.9999.min"}, {"detail": "int", "kind": 5, "label": "nbytes", "sortText": "0.9999.nbytes"}, {"detail": "int", "kind": 5, "label": "ndim", "sortText": "0.9999.ndim"}, {"detail": "\n@overload\ndef nonzero(self: ndarray[tuple[Never, Never, Never, Never]]) -> tuple[ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]], ...]: ...\ndef nonzero(self: ndarray[tuple[int]]) -> tuple[ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]]: ...\ndef nonzero(self: ndarray[tuple[int, int]]) -> tuple[ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]], ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]]: ...\ndef nonzero(self: ndarray[tuple[int, int, int]]) -> tuple[ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]], ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]], ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]]: ...\ndef nonzero(self: ndarray) -> tuple[ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]], ...]: ...", "kind": 2, "label": "nonzero", "sortText": "0.9999.nonzero"}, {"detail": "\n@overload\ndef partition(\n self : ndarray,\n kth : _ArrayLikeInt,\n /,\n axis : SupportsIndex = -1,\n kind : Literal['introselect'] = 'introselect',\n order: None = None\n) -> None: ...\ndef partition(\n self : ndarray[tuple[Any, ...], dtype[void]],\n kth : _ArrayLikeInt,\n /,\n axis : SupportsIndex = -1,\n kind : Literal['introselect'] = 'introselect',\n order: Sequence[str] | str | None = None\n) -> None: ...", "kind": 2, "label": "partition", "sortText": "0.9999.partition"}, {"detail": "\n@overload\ndef prod[ScalarT: inexact](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef prod[ScalarT: inexact | object_](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef prod[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | object_]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | int | integer = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> signedinteger[_NBitIntP]: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | int | integer = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | int | integer = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[object_]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef prod[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef prod[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...] | None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef prod[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...] | None,\n dtype : _DTypeLike,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef prod[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...],\n dtype : _DTypeLike,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef prod[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT,\n keepdims: _NoValueType | builtins.bool = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef prod[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...] | None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None,\n out : ArrayT,\n *,\n keepdims: _NoValueType | builtins.bool = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...],\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray: ...", "kind": 2, "label": "prod", "sortText": "0.9999.prod"}, {"detail": "def put(\n self : ndarray,\n indices: _ArrayLikeInt_co,\n values : ArrayLike,\n /,\n mode : _ModeKind = 'raise'\n) -> None: ...", "kind": 2, "label": "put", "sortText": "0.9999.put"}, {"detail": "def ravel(\n self : ndarray,\n /,\n order: _OrderKACF = 'C'\n) -> ndarray[tuple[int]]: ...", "kind": 2, "label": "ravel", "sortText": "0.9999.ravel"}, {"detail": "ndarray", "kind": 5, "label": "real", "sortText": "0.9999.real"}, {"detail": "\n@overload\ndef repeat(\n self : ndarray,\n repeats: _ArrayLikeInt_co,\n /,\n axis : None = None\n) -> ndarray[tuple[int]]: ...\ndef repeat(\n self: ndarray,\n repeats: _ArrayLikeInt_co,\n /,\n axis: SupportsIndex\n) -> ndarray: ...", "kind": 2, "label": "repeat", "sortText": "0.9999.repeat"}, {"detail": "\n@overload\ndef reshape(\n self : ndarray,\n shape: None,\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray: ...\ndef reshape(\n self : ndarray,\n shape: Sequence[Never],\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray[tuple[()]]: ...\ndef reshape[AnyShapeT: (tuple[()], tuple[int], tuple[int, int], tuple[int, int, int], tuple[int, int, int, int], tuple[int, int, int, int, int], tuple[int, int, int, int, int, int], tuple[int, int, int, int, int, int, int], tuple[int, int, int, int, int, int, int, int])](\n self : ndarray,\n shape: AnyShapeT,\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray[AnyShapeT]: ...\ndef reshape(\n self : ndarray,\n size1: SupportsIndex,\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray[tuple[int]]: ...\ndef reshape(\n self : ndarray,\n size1: SupportsIndex,\n size2: SupportsIndex,\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray[tuple[int, int]]: ...\ndef reshape(\n self : ndarray,\n size1: SupportsIndex,\n size2: SupportsIndex,\n size3: SupportsIndex,\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray[tuple[int, int, int]]: ...\ndef reshape(\n self : ndarray,\n size1: SupportsIndex,\n size2: SupportsIndex,\n size3: SupportsIndex,\n size4: SupportsIndex,\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray[tuple[int, int, int, int]]: ...\ndef reshape(\n self : ndarray,\n size0 : SupportsIndex,\n /,\n *shape: SupportsIndex,\n *,\n order : _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray: ...\ndef reshape(\n self : ndarray,\n shape: Sequence[SupportsIndex],\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray: ...", "kind": 2, "label": "reshape", "sortText": "0.9999.reshape"}, {"detail": "\n@overload\ndef resize(\n self : ndarray,\n new_shape: _ShapeLike,\n /,\n *,\n refcheck : bool = True\n) -> None: ...\ndef resize(\n self : ndarray,\n /,\n *new_shape: SupportsIndex,\n *,\n refcheck : bool = True\n) -> None: ...", "kind": 2, "label": "resize", "sortText": "0.9999.resize"}, {"detail": "\n@overload\ndef round(\n self : ndarray,\n /,\n decimals: SupportsIndex = 0,\n out : None = None\n) -> ndarray: ...\ndef round[ArrayT: ndarray](\n self: ndarray,\n /,\n decimals: SupportsIndex,\n out: ArrayT\n) -> ArrayT: ...\ndef round[ArrayT: ndarray](\n self : ndarray,\n /,\n decimals: SupportsIndex = 0,\n *,\n out : ArrayT\n) -> ArrayT: ...", "kind": 2, "label": "round", "sortText": "0.9999.round"}, {"detail": "\n@overload\ndef searchsorted(\n self : ndarray,\n v : _ScalarLike_co,\n /,\n side : _SortSide = 'left',\n sorter: _NestedSequence[_SupportsArray[dtype[numpy.bool | integer]]] | _NestedSequence[int] | _SupportsArray[dtype[numpy.bool | integer]] | int | None = None\n) -> signedinteger[_NBitIntP]: ...\ndef searchsorted(\n self : ndarray,\n v : ArrayLike,\n /,\n side : _SortSide = 'left',\n sorter: _NestedSequence[_SupportsArray[dtype[numpy.bool | integer]]] | _NestedSequence[int] | _SupportsArray[dtype[numpy.bool | integer]] | int | None = None\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...", "kind": 2, "label": "searchsorted", "sortText": "0.9999.searchsorted"}, {"detail": "def setfield(\n self : ndarray,\n val : ArrayLike,\n /,\n dtype : DTypeLike,\n offset: SupportsIndex = 0\n) -> None: ...", "kind": 2, "label": "setfield", "sortText": "0.9999.setfield"}, {"detail": "def setflags(\n self : ndarray,\n /,\n *,\n write: bool | None = None,\n align: bool | None = None,\n uic : bool | None = None\n) -> None: ...", "kind": 2, "label": "setflags", "sortText": "0.9999.setflags"}, {"detail": "tuple[Any, ...]", "kind": 5, "label": "shape", "sortText": "0.9999.shape"}, {"detail": "int", "kind": 5, "label": "size", "sortText": "0.9999.size"}, {"detail": "def sort(\n self : ndarray,\n /,\n axis : SupportsIndex = -1,\n kind : Literal['H', 'M', 'Q', 'S', 'heap', 'heapsort', 'merge', 'mergesort', 'quick', 'quicksort', 'stable', 'stablesort'] | None = None,\n order : Sequence[str] | str | None = None,\n *,\n stable : bool | None = None,\n descending: bool | None = None\n) -> None: ...", "kind": 2, "label": "sort", "sortText": "0.9999.sort"}, {"detail": "def squeeze(\n self: ndarray,\n /,\n axis: SupportsIndex | tuple[SupportsIndex, ...] | None = ...\n) -> ndarray: ...", "kind": 2, "label": "squeeze", "sortText": "0.9999.squeeze"}, {"detail": "\n@overload\ndef std(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | complex128 | integer | object_]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> float64: ...\ndef std(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | complex128 | integer]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef std(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | complex128 | integer]],\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef std[ScalarT: floating | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ScalarT: ...\ndef std[ScalarT: floating | object_ | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef std[ArrayT: ndarray[tuple[Any, ...], dtype[floating | object_ | timedelta64]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ArrayT: ...\ndef std[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ScalarT: ...\ndef std[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef std[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None,\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef std[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef std[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT,\n ddof : float = 0,\n keepdims : _NoValueType | builtins.bool = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ArrayT: ...\ndef std(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> Any: ...\ndef std(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray: ...\ndef std(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray: ...", "kind": 2, "label": "std", "sortText": "0.9999.std"}, {"detail": "tuple[int, ...]", "kind": 5, "label": "strides", "sortText": "0.9999.strides"}, {"detail": "\n@overload\ndef sum[ScalarT: inexact | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef sum[ScalarT: inexact | object_ | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef sum[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | object_ | timedelta64]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | int | integer = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> signedinteger[_NBitIntP]: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | int | integer = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | int | integer = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[object_]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef sum[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef sum[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef sum[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None,\n dtype : _DTypeLike,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef sum[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _DTypeLike,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef sum[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT,\n keepdims: _NoValueType | builtins.bool = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef sum[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None,\n out : ArrayT,\n *,\n keepdims: _NoValueType | builtins.bool = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray: ...", "kind": 2, "label": "sum", "sortText": "0.9999.sum"}, {"detail": "def swapaxes(\n self: ndarray,\n axis1: SupportsIndex,\n axis2: SupportsIndex,\n /\n) -> ndarray: ...", "kind": 2, "label": "swapaxes", "sortText": "0.9999.swapaxes"}, {"detail": "\n@overload\ndef take[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n indices: _IntLike_co,\n /,\n axis : SupportsIndex | None = ...,\n out : None = None,\n mode : _ModeKind = ...\n) -> ScalarT: ...\ndef take(\n self : ndarray,\n indices: _ArrayLikeInt_co,\n /,\n axis : SupportsIndex | None = ...,\n out : None = None,\n mode : _ModeKind = ...\n) -> ndarray: ...\ndef take[ArrayT: ndarray](\n self : ndarray,\n indices: _ArrayLikeInt_co,\n /,\n axis : SupportsIndex | None = ...,\n *,\n out : ArrayT,\n mode : _ModeKind = ...\n) -> ArrayT: ...\ndef take[ArrayT: ndarray](\n self : ndarray,\n indices: _ArrayLikeInt_co,\n /,\n axis : SupportsIndex | None,\n out : ArrayT,\n mode : _ModeKind = ...\n) -> ArrayT: ...", "kind": 2, "label": "take", "sortText": "0.9999.take"}, {"detail": "def to_device(\n self : ndarray,\n device: Literal['cpu'],\n /,\n *,\n stream: int | Any | None = ...\n) -> ndarray: ...", "kind": 2, "label": "to_device", "sortText": "0.9999.to_device"}, {"detail": "def tobytes(\n self : ndarray,\n order: _OrderKACF = ...\n) -> bytes: ...", "kind": 2, "label": "tobytes", "sortText": "0.9999.tobytes"}, {"detail": "def tofile(\n self : ndarray,\n fid : PathLike[bytes] | PathLike[str] | _SupportsFileMethods | bytes | str,\n /,\n sep : str = '',\n format: str = '%s'\n) -> None: ...", "kind": 2, "label": "tofile", "sortText": "0.9999.tofile"}, {"detail": "\n@overload\ndef tolist[T](self: ndarray[tuple[Never], dtype[generic[T]]], /) -> Any: ...\ndef tolist[T](self: ndarray[tuple[()], dtype[generic[T]]], /) -> T: ...\ndef tolist[T](self: ndarray[tuple[int], dtype[generic[T]]], /) -> list[T]: ...\ndef tolist[T](self: ndarray[tuple[int, int], dtype[generic[T]]], /) -> list[list[T]]: ...\ndef tolist[T](self: ndarray[tuple[int, int, int], dtype[generic[T]]], /) -> list[list[list[T]]]: ...\ndef tolist(self: ndarray, /) -> Any: ...", "kind": 2, "label": "tolist", "sortText": "0.9999.tolist"}, {"detail": "\n@overload\ndef trace(\n self : ndarray,\n /,\n offset: SupportsIndex = 0,\n axis1 : SupportsIndex = 0,\n axis2 : SupportsIndex = 1,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None\n) -> Any: ...\ndef trace[ArrayT: ndarray](\n self : ndarray,\n /,\n offset: SupportsIndex = 0,\n axis1 : SupportsIndex = 0,\n axis2 : SupportsIndex = 1,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT\n) -> ArrayT: ...\ndef trace[ArrayT: ndarray](\n self: ndarray,\n /,\n offset: SupportsIndex,\n axis1: SupportsIndex,\n axis2: SupportsIndex,\n dtype: _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None,\n out: ArrayT\n) -> ArrayT: ...", "kind": 2, "label": "trace", "sortText": "0.9999.trace"}, {"detail": "\n@overload\ndef transpose(\n self: ndarray,\n axes: Sequence[SupportsIndex] | SupportsIndex | None,\n /\n) -> ndarray: ...\ndef transpose(\n self: ndarray,\n /,\n *axes: SupportsIndex\n) -> ndarray: ...", "kind": 2, "label": "transpose", "sortText": "0.9999.transpose"}, {"detail": "\n@overload\ndef var(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | complex128 | integer | object_]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> float64: ...\ndef var(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | complex128 | integer]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef var(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | complex128 | integer]],\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef var[ScalarT: floating | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ScalarT: ...\ndef var[ScalarT: floating | object_ | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef var[ArrayT: ndarray[tuple[Any, ...], dtype[floating | object_ | timedelta64]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ArrayT: ...\ndef var[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ScalarT: ...\ndef var[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef var[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None,\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef var[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef var[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT,\n ddof : float = 0,\n keepdims : _NoValueType | builtins.bool = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ArrayT: ...\ndef var(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> Any: ...\ndef var(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray: ...\ndef var(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray: ...", "kind": 2, "label": "var", "sortText": "0.9999.var"}, {"detail": "\n@overload\ndef view(self: ndarray, /) -> ndarray: ...\ndef view[DTypeT: dtype](\n self: ndarray,\n /,\n dtype: numpy._HasDType[DTypeT] | DTypeT\n) -> ndarray[tuple[Any, ...], DTypeT]: ...\ndef view[ScalarT: generic](\n self: ndarray,\n /,\n dtype: _DTypeLike\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef view[ArrayT: ndarray](\n self: ndarray,\n /,\n *,\n type: type[ArrayT]\n) -> ArrayT: ...\ndef view[ArrayT: ndarray](\n self: ndarray,\n /,\n dtype: type[ArrayT]\n) -> ArrayT: ...\ndef view(\n self: ndarray,\n /,\n dtype: DTypeLike\n) -> ndarray: ...\ndef view[ArrayT: ndarray](\n self: ndarray,\n /,\n dtype: DTypeLike,\n type: type[ArrayT]\n) -> ArrayT: ...", "kind": 2, "label": "view", "sortText": "0.9999.view"}, {"detail": "\n@overload\ndef __abs__[ShapeT: tuple[int, ...], NBitT: NBitBase](self: ndarray[ShapeT, dtype[complexfloating[NBitT, NBitT]]], /) -> ndarray[ShapeT, dtype[floating[NBitT]]]: ...\ndef __abs__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer | object_ | timedelta64]]](self: ArrayT, /) -> ArrayT: ...", "kind": 2, "label": "__abs__", "sortText": "3.9999.__abs__"}, {"detail": "\n@overload\ndef __add__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __add__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __add__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[datetime64]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[datetime64]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[bytes_]],\n other: _ArrayLikeBytes_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[bytes_]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[str_]],\n other: _ArrayLikeStr_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[str_]]: ...\ndef __add__(\n self: ndarray[Any, StringDType],\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ndarray[tuple[Any, ...], StringDType]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __add__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__add__", "sortText": "3.9999.__add__"}, {"detail": "\n@overload\ndef __and__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __and__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __and__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __and__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __and__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__and__", "sortText": "3.9999.__and__"}, {"detail": "\n@overload\ndef __array__(\n self : ndarray,\n dtype: None = None,\n /,\n *,\n copy : bool | None = None\n) -> ndarray: ...\ndef __array__[DTypeT: dtype](\n self : ndarray,\n dtype: DTypeT,\n /,\n *,\n copy : bool | None = None\n) -> ndarray[tuple[Any, ...], DTypeT]: ...", "kind": 2, "label": "__array__", "sortText": "3.9999.__array__"}, {"detail": "def __array_finalize__(\n self: ndarray,\n obj: ndarray | None,\n /\n) -> None: ...", "kind": 2, "label": "__array_finalize__", "sortText": "3.9999.__array_finalize__"}, {"detail": "def __array_function__(\n self: ndarray,\n func: (...) -> Any,\n types: Iterable[type[Any]],\n args: Iterable[Any],\n kwargs: Mapping[str, Any]\n) -> Any: ...", "kind": 2, "label": "__array_function__", "sortText": "3.9999.__array_function__"}, {"detail": "dict[str, Any]", "kind": 5, "label": "__array_interface__", "sortText": "3.9999.__array_interface__"}, {"detail": "def __array_namespace__(\n self : ndarray,\n /,\n *,\n api_version: Literal['2021.12', '2022.12', '2023.12', '2024.12', '2025.12'] | None = None\n) -> ModuleType: ...", "kind": 2, "label": "__array_namespace__", "sortText": "3.9999.__array_namespace__"}, {"detail": "float", "kind": 5, "label": "__array_priority__", "sortText": "3.9999.__array_priority__"}, {"detail": "CapsuleType", "kind": 5, "label": "__array_struct__", "sortText": "3.9999.__array_struct__"}, {"detail": "def __array_ufunc__(\n self: ndarray,\n ufunc: ufunc,\n method: Literal['__call__', 'accumulate', 'at', 'outer', 'reduce', 'reduceat'],\n /,\n *inputs: Any,\n **kwargs: Any\n) -> Any: ...", "kind": 2, "label": "__array_ufunc__", "sortText": "3.9999.__array_ufunc__"}, {"detail": "def __array_wrap__[ShapeT: tuple[int, ...], DTypeT: dtype](\n self : ndarray,\n array : ndarray[ShapeT, DTypeT],\n context : tuple[ufunc, tuple[Any, ...], int] | None = ...,\n return_scalar: bool = ...,\n /\n) -> ndarray[ShapeT, DTypeT]: ...", "kind": 2, "label": "__array_wrap__", "sortText": "3.9999.__array_wrap__"}, {"detail": "def __bool__(self: ndarray, /) -> bool: ...", "kind": 2, "label": "__bool__", "sortText": "3.9999.__bool__"}, {"detail": "def __buffer__(\n self: ndarray,\n flags: int,\n /\n) -> memoryview: ...", "kind": 2, "label": "__buffer__", "sortText": "3.9999.__buffer__"}, {"detail": "def __class_getitem__(\n cls: type[ndarray],\n item: Any,\n /\n) -> GenericAlias: ...", "kind": 2, "label": "__class_getitem__", "sortText": "3.9999.__class_getitem__"}, {"detail": "def __complex__(self: ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]], /) -> complex: ...", "kind": 2, "label": "__complex__", "sortText": "3.9999.__complex__"}, {"detail": "def __contains__(\n self: ndarray,\n value: object,\n /\n) -> bool: ...", "kind": 2, "label": "__contains__", "sortText": "3.9999.__contains__"}, {"detail": "def __copy__(self: ndarray) -> ndarray: ...", "kind": 2, "label": "__copy__", "sortText": "3.9999.__copy__"}, {"detail": "def __deepcopy__(\n self: ndarray,\n memo: dict[int, Any] | None,\n /\n) -> ndarray: ...", "kind": 2, "label": "__deepcopy__", "sortText": "3.9999.__deepcopy__"}, {"detail": "\n@overload\ndef __divmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n rhs: numpy.bool | int,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[ScalarT]], ndarray[tuple[Any, ...], dtype[ScalarT]]]: ...\ndef __divmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n rhs: _ArrayLikeBool_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[ScalarT]], ndarray[tuple[Any, ...], dtype[ScalarT]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n rhs: _ArrayLikeBool_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]]: ...\ndef __divmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n rhs: _ArrayLike,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[ScalarT]], ndarray[tuple[Any, ...], dtype[ScalarT]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n rhs: _ArrayLikeFloat64_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n rhs: _ArrayLike,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n rhs: _ArrayLikeUInt_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[unsignedinteger]], ndarray[tuple[Any, ...], dtype[unsignedinteger]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n rhs: _ArrayLikeInt_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[signedinteger]], ndarray[tuple[Any, ...], dtype[signedinteger]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n rhs: _ArrayLikeFloat_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[floating]], ndarray[tuple[Any, ...], dtype[floating]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n rhs: _ArrayLike,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[signedinteger[_64Bit]]], ndarray[tuple[Any, ...], dtype[timedelta64]]]: ...", "kind": 2, "label": "__divmod__", "sortText": "3.9999.__divmod__"}, {"detail": "def __dlpack__(\n self : ndarray[tuple[Any, ...], dtype[number]],\n /,\n *,\n stream : int | Any | None = None,\n max_version: tuple[int, int] | None = None,\n dl_device : tuple[int, int] | None = None,\n copy : bool | None = None\n) -> CapsuleType: ...", "kind": 2, "label": "__dlpack__", "sortText": "3.9999.__dlpack__"}, {"detail": "def __dlpack_device__(self: ndarray, /) -> tuple[Literal[1], Literal[0]]: ...", "kind": 2, "label": "__dlpack_device__", "sortText": "3.9999.__dlpack_device__"}, {"detail": "def __eq__(\n self: ndarray,\n other: Any,\n /\n) -> Any: ...", "kind": 2, "label": "__eq__", "sortText": "3.9999.__eq__"}, {"detail": "def __float__(self: ndarray, /) -> float: ...", "kind": 2, "label": "__float__", "sortText": "3.9999.__float__"}, {"detail": "\n@overload\ndef __floordiv__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __floordiv__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __floordiv__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_64Bit]]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLikeBool_co,\n /\n) -> NoReturn: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __floordiv__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__floordiv__", "sortText": "3.9999.__floordiv__"}, {"detail": "\n@overload\ndef __ge__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ge__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ge__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ge__(\n self: ndarray[tuple[Any, ...], dtype[bytes_]],\n other: _ArrayLikeBytes_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ge__(\n self: ndarray[tuple[Any, ...], StringDType | dtype[str_]],\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ge__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: object,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ge__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...", "kind": 2, "label": "__ge__", "sortText": "3.9999.__ge__"}, {"detail": "\n@overload\ndef __getitem__(\n self: ndarray,\n key: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]] | tuple[ndarray[tuple[Any, ...], dtype[numpy.bool | integer]], ...],\n /\n) -> ndarray: ...\ndef __getitem__(\n self: ndarray,\n key: SupportsIndex | tuple[SupportsIndex, ...],\n /\n) -> Any: ...\ndef __getitem__(\n self: ndarray,\n key: _ToIndices,\n /\n) -> ndarray: ...\ndef __getitem__(\n self: ndarray[tuple[Any, ...], dtype[void]],\n key: str,\n /\n) -> ndarray: ...\ndef __getitem__(\n self: ndarray[tuple[Any, ...], dtype[void]],\n key: list[str],\n /\n) -> ndarray[tuple[Any, ...], dtype[void]]: ...", "kind": 2, "label": "__getitem__", "sortText": "3.9999.__getitem__"}, {"detail": "\n@overload\ndef __gt__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __gt__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __gt__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __gt__(\n self: ndarray[tuple[Any, ...], dtype[bytes_]],\n other: _ArrayLikeBytes_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __gt__(\n self: ndarray[tuple[Any, ...], StringDType | dtype[str_]],\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __gt__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: object,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __gt__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...", "kind": 2, "label": "__gt__", "sortText": "3.9999.__gt__"}, {"detail": "None", "kind": 5, "label": "__hash__", "sortText": "3.9999.__hash__"}, {"detail": "\n@overload\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool]]](\n self: ArrayT,\n other: _ArrayLikeBool_co,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[complexfloating]]](\n self: ArrayT,\n other: _ArrayLikeComplex_co,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[inexact]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[number]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[datetime64 | timedelta64]]](\n self: ArrayT,\n other: _ArrayLikeTD64_co,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[bytes_]]](\n self: ArrayT,\n other: _ArrayLikeBytes_co,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], StringDType | dtype[str_]]](\n self: ArrayT,\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__iadd__", "sortText": "3.9999.__iadd__"}, {"detail": "\n@overload\ndef __iand__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool]]](\n self: ArrayT,\n other: _ArrayLikeBool_co,\n /\n) -> ArrayT: ...\ndef __iand__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __iand__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__iand__", "sortText": "3.9999.__iand__"}, {"detail": "\n@overload\ndef __ifloordiv__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __ifloordiv__[ArrayT: ndarray[tuple[Any, ...], dtype[floating | timedelta64]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __ifloordiv__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__ifloordiv__", "sortText": "3.9999.__ifloordiv__"}, {"detail": "\n@overload\ndef __ilshift__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __ilshift__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__ilshift__", "sortText": "3.9999.__ilshift__"}, {"detail": "\n@overload\ndef __imatmul__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool]]](\n self: ArrayT,\n other: _ArrayLikeBool_co,\n /\n) -> ArrayT: ...\ndef __imatmul__[ArrayT: ndarray[tuple[Any, ...], dtype[complexfloating]]](\n self: ArrayT,\n other: _ArrayLikeComplex_co,\n /\n) -> ArrayT: ...\ndef __imatmul__[ArrayT: ndarray[tuple[Any, ...], dtype[inexact]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __imatmul__[ArrayT: ndarray[tuple[Any, ...], dtype[number]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __imatmul__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__imatmul__", "sortText": "3.9999.__imatmul__"}, {"detail": "\n@overload\ndef __imod__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __imod__[ArrayT: ndarray[tuple[Any, ...], dtype[floating]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __imod__[ArrayT: ndarray[tuple[Any, ...], dtype[timedelta64]]](\n self: ArrayT,\n other: _ArrayLike,\n /\n) -> ArrayT: ...\ndef __imod__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__imod__", "sortText": "3.9999.__imod__"}, {"detail": "\n@overload\ndef __imul__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool]]](\n self: ArrayT,\n other: _ArrayLikeBool_co,\n /\n) -> ArrayT: ...\ndef __imul__[ArrayT: ndarray[tuple[Any, ...], dtype[complexfloating]]](\n self: ArrayT,\n other: _ArrayLikeComplex_co,\n /\n) -> ArrayT: ...\ndef __imul__[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | timedelta64]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __imul__[ArrayT: ndarray[tuple[Any, ...], dtype[character | number]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __imul__[ArrayT: ndarray[tuple[Any, ...], StringDType | dtype[str_]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __imul__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__imul__", "sortText": "3.9999.__imul__"}, {"detail": "def __index__(self: ndarray[tuple[Any, ...], dtype[integer]], /) -> int: ...", "kind": 2, "label": "__index__", "sortText": "3.9999.__index__"}, {"detail": "def __int__(self: ndarray, /) -> int: ...", "kind": 2, "label": "__int__", "sortText": "3.9999.__int__"}, {"detail": "def __invert__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | object_]]](self: ArrayT, /) -> ArrayT: ...", "kind": 2, "label": "__invert__", "sortText": "3.9999.__invert__"}, {"detail": "\n@overload\ndef __ior__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool]]](\n self: ArrayT,\n other: _ArrayLikeBool_co,\n /\n) -> ArrayT: ...\ndef __ior__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __ior__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__ior__", "sortText": "3.9999.__ior__"}, {"detail": "\n@overload\ndef __ipow__[ArrayT: ndarray[tuple[Any, ...], dtype[complexfloating]]](\n self: ArrayT,\n other: _ArrayLikeComplex_co,\n /\n) -> ArrayT: ...\ndef __ipow__[ArrayT: ndarray[tuple[Any, ...], dtype[inexact]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __ipow__[ArrayT: ndarray[tuple[Any, ...], dtype[number]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __ipow__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__ipow__", "sortText": "3.9999.__ipow__"}, {"detail": "\n@overload\ndef __irshift__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __irshift__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__irshift__", "sortText": "3.9999.__irshift__"}, {"detail": "\n@overload\ndef __isub__[ArrayT: ndarray[tuple[Any, ...], dtype[complexfloating]]](\n self: ArrayT,\n other: _ArrayLikeComplex_co,\n /\n) -> ArrayT: ...\ndef __isub__[ArrayT: ndarray[tuple[Any, ...], dtype[inexact]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __isub__[ArrayT: ndarray[tuple[Any, ...], dtype[number]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __isub__[ArrayT: ndarray[tuple[Any, ...], dtype[datetime64 | timedelta64]]](\n self: ArrayT,\n other: _ArrayLikeTD64_co,\n /\n) -> ArrayT: ...\ndef __isub__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__isub__", "sortText": "3.9999.__isub__"}, {"detail": "\n@overload\ndef __iter__(self: ndarray[tuple[Never, Never, Never, Never, Never, Never, Never, Never, Never], Any], /) -> Iterator[Any]: ...\ndef __iter__[ScalarT: _ScalarNotObject](self: ndarray[tuple[int], dtype[ScalarT]], /) -> Iterator[ScalarT]: ...\ndef __iter__(self: ndarray[tuple[int], StringDType], /) -> Iterator[str]: ...\ndef __iter__[DTypeT: dtype](self: ndarray[tuple[int, int, *tuple[int, ...]], DTypeT], /) -> Iterator[ndarray[tuple[Any, ...], DTypeT]]: ...\ndef __iter__(self: ndarray, /) -> Iterator[Any]: ...", "kind": 2, "label": "__iter__", "sortText": "3.9999.__iter__"}, {"detail": "\n@overload\ndef __itruediv__[ArrayT: ndarray[tuple[Any, ...], dtype[complexfloating]]](\n self: ArrayT,\n other: _ArrayLikeComplex_co,\n /\n) -> ArrayT: ...\ndef __itruediv__[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | timedelta64]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __itruediv__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__itruediv__", "sortText": "3.9999.__itruediv__"}, {"detail": "\n@overload\ndef __ixor__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool]]](\n self: ArrayT,\n other: _ArrayLikeBool_co,\n /\n) -> ArrayT: ...\ndef __ixor__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __ixor__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__ixor__", "sortText": "3.9999.__ixor__"}, {"detail": "\n@overload\ndef __le__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __le__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __le__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __le__(\n self: ndarray[tuple[Any, ...], dtype[bytes_]],\n other: _ArrayLikeBytes_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __le__(\n self: ndarray[tuple[Any, ...], StringDType | dtype[str_]],\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __le__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: object,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __le__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...", "kind": 2, "label": "__le__", "sortText": "3.9999.__le__"}, {"detail": "def __len__(self: ndarray) -> int: ...", "kind": 2, "label": "__len__", "sortText": "3.9999.__len__"}, {"detail": "\n@overload\ndef __lshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __lshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __lshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __lshift__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __lshift__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__lshift__", "sortText": "3.9999.__lshift__"}, {"detail": "\n@overload\ndef __lt__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __lt__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __lt__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __lt__(\n self: ndarray[tuple[Any, ...], dtype[bytes_]],\n other: _ArrayLikeBytes_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __lt__(\n self: ndarray[tuple[Any, ...], StringDType | dtype[str_]],\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __lt__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: object,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __lt__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...", "kind": 2, "label": "__lt__", "sortText": "3.9999.__lt__"}, {"detail": "\n@overload\ndef __matmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __matmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[floating[_64Bit]]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[complexfloating[_64Bit, _64Bit]]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __matmul__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__matmul__", "sortText": "3.9999.__matmul__"}, {"detail": "\n@overload\ndef __mod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __mod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __mod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __mod__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__mod__", "sortText": "3.9999.__mod__"}, {"detail": "\n@overload\ndef __mul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __mul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __mul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __mul__(\n self: ndarray[Any, StringDType | dtype[character]],\n other: _ArrayLikeInt,\n /\n) -> ndarray: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __mul__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__mul__", "sortText": "3.9999.__mul__"}, {"detail": "def __ne__(\n self: ndarray,\n other: Any,\n /\n) -> Any: ...", "kind": 2, "label": "__ne__", "sortText": "3.9999.__ne__"}, {"detail": "def __neg__[ArrayT: ndarray[tuple[Any, ...], dtype[number | object_ | timedelta64]]](self: ArrayT, /) -> ArrayT: ...", "kind": 2, "label": "__neg__", "sortText": "3.9999.__neg__"}, {"detail": "def __new__[Self@ndarray: ndarray](\n cls : type[Self@ndarray],\n shape : _ShapeLike,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = ...,\n buffer : Buffer | None = ...,\n offset : SupportsIndex = ...,\n strides: Sequence[SupportsIndex] | SupportsIndex | None = ...,\n order : _OrderKACF = ...\n) -> Self@ndarray: ...", "kind": 5, "label": "__new__", "sortText": "3.9999.__new__"}, {"detail": "\n@overload\ndef __or__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __or__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __or__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __or__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __or__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__or__", "sortText": "3.9999.__or__"}, {"detail": "def __pos__[ArrayT: ndarray[tuple[Any, ...], dtype[number | object_ | timedelta64]]](self: ArrayT, /) -> ArrayT: ...", "kind": 2, "label": "__pos__", "sortText": "3.9999.__pos__"}, {"detail": "\n@overload\ndef __pow__[ScalarT: number](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __pow__[ScalarT: number](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __pow__[ScalarT: number](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n mod : None = None,\n /\n) -> Any: ...\ndef __pow__(\n self : ndarray,\n other: _ArrayLikeObject_co,\n mod : None = None,\n /\n) -> Any: ...", "kind": 2, "label": "__pow__", "sortText": "3.9999.__pow__"}, {"detail": "\n@overload\ndef __radd__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __radd__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __radd__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[datetime64]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[datetime64]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[bytes_]],\n other: _ArrayLikeBytes_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[bytes_]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[str_]],\n other: _ArrayLikeStr_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[str_]]: ...\ndef __radd__(\n self: ndarray[Any, StringDType],\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ndarray[tuple[Any, ...], StringDType]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __radd__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__radd__", "sortText": "3.9999.__radd__"}, {"detail": "\n@overload\ndef __rand__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __rand__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rand__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rand__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rand__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rand__", "sortText": "3.9999.__rand__"}, {"detail": "\n@overload\ndef __rdivmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n lhs: numpy.bool | int,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[ScalarT]], ndarray[tuple[Any, ...], dtype[ScalarT]]]: ...\ndef __rdivmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n lhs: _ArrayLikeBool_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[ScalarT]], ndarray[tuple[Any, ...], dtype[ScalarT]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n lhs: _ArrayLikeBool_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]]: ...\ndef __rdivmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n lhs: _ArrayLike,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[ScalarT]], ndarray[tuple[Any, ...], dtype[ScalarT]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n lhs: _ArrayLikeFloat64_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n lhs: _ArrayLike,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n lhs: _ArrayLikeUInt_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[unsignedinteger]], ndarray[tuple[Any, ...], dtype[unsignedinteger]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n lhs: _ArrayLikeInt_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[signedinteger]], ndarray[tuple[Any, ...], dtype[signedinteger]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n lhs: _ArrayLikeFloat_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[floating]], ndarray[tuple[Any, ...], dtype[floating]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n lhs: _ArrayLike,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[signedinteger[_64Bit]]], ndarray[tuple[Any, ...], dtype[timedelta64]]]: ...", "kind": 2, "label": "__rdivmod__", "sortText": "3.9999.__rdivmod__"}, {"detail": "\n@overload\ndef __rfloordiv__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rfloordiv__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __rfloordiv__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_64Bit]]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[floating | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rfloordiv__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rfloordiv__", "sortText": "3.9999.__rfloordiv__"}, {"detail": "\n@overload\ndef __rlshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __rlshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rlshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rlshift__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rlshift__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rlshift__", "sortText": "3.9999.__rlshift__"}, {"detail": "\n@overload\ndef __rmatmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __rmatmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[floating[_64Bit]]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[complexfloating[_64Bit, _64Bit]]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rmatmul__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rmatmul__", "sortText": "3.9999.__rmatmul__"}, {"detail": "\n@overload\ndef __rmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __rmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rmod__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rmod__", "sortText": "3.9999.__rmod__"}, {"detail": "\n@overload\ndef __rmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __rmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rmul__(\n self: ndarray[Any, StringDType | dtype[character]],\n other: _ArrayLikeInt,\n /\n) -> ndarray: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rmul__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rmul__", "sortText": "3.9999.__rmul__"}, {"detail": "\n@overload\ndef __ror__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ror__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __ror__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __ror__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __ror__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__ror__", "sortText": "3.9999.__ror__"}, {"detail": "\n@overload\ndef __rpow__[ScalarT: number](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rpow__[ScalarT: number](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __rpow__[ScalarT: number](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n mod : None = None,\n /\n) -> Any: ...\ndef __rpow__(\n self : ndarray,\n other: _ArrayLikeObject_co,\n mod : None = None,\n /\n) -> Any: ...", "kind": 2, "label": "__rpow__", "sortText": "3.9999.__rpow__"}, {"detail": "\n@overload\ndef __rrshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __rrshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rrshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rrshift__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rrshift__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rrshift__", "sortText": "3.9999.__rrshift__"}, {"detail": "\n@overload\ndef __rshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __rshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rshift__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rshift__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rshift__", "sortText": "3.9999.__rshift__"}, {"detail": "\n@overload\ndef __rsub__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rsub__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> NoReturn: ...\ndef __rsub__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[datetime64]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rsub__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rsub__", "sortText": "3.9999.__rsub__"}, {"detail": "\n@overload\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]] | ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _NestedSequence[_SupportsArray[dtype[numpy.bool | integer]]] | _NestedSequence[_SupportsArray[dtype[floating[_64Bit]]]] | _NestedSequence[int] | _SupportsArray[dtype[numpy.bool | integer]] | _SupportsArray[dtype[floating[_64Bit]]] | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[floating]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[complexfloating]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[inexact]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[inexact]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[floating | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rtruediv__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rtruediv__", "sortText": "3.9999.__rtruediv__"}, {"detail": "\n@overload\ndef __rxor__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __rxor__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rxor__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rxor__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rxor__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rxor__", "sortText": "3.9999.__rxor__"}, {"detail": "\n@overload\ndef __setitem__(\n self: ndarray[Any, StringDType | dtype[numpy.bool | flexible | object_]],\n key: _ToIndices,\n value: object,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray[tuple[Any, ...], dtype[integer]],\n key: _ToIndices,\n value: SupportsIndex | SupportsInt | _NestedSequence[_ConvertibleToInt] | _NestedSequence[_SupportsArray[dtype[numpy.bool | integer]]] | _NestedSequence[int] | _SupportsArray[dtype[numpy.bool | integer]] | bytes | int | str,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray[tuple[Any, ...], dtype[floating]],\n key: _ToIndices,\n value: SupportsFloat | SupportsIndex | _NestedSequence[SupportsFloat | SupportsIndex | bytes | str | None] | _NestedSequence[_SupportsArray[dtype[numpy.bool | floating | integer]]] | _NestedSequence[float] | _SupportsArray[dtype[numpy.bool | floating | integer]] | bytes | float | str | None,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray[tuple[Any, ...], dtype[complexfloating]],\n key: _ToIndices,\n value: SupportsComplex | SupportsFloat | SupportsIndex | _NestedSequence[SupportsComplex | SupportsFloat | SupportsIndex | bytes | str | None] | _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _SupportsArray[dtype[numpy.bool | number]] | bytes | complex | str | None,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n key: _ToIndices,\n value: _NestedSequence[_ConvertibleToTD64] | numpy.bool | bytes | character | int | number | str | timedelta | timedelta64 | None,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n key: _ToIndices,\n value: _NestedSequence[_ConvertibleToDT64] | numpy.bool | bytes | character | date | datetime64 | int | number | str | None,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray[tuple[Any, ...], dtype[void]],\n key: list[str] | str,\n value: object,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray,\n key: _ToIndices,\n value: ArrayLike,\n /\n) -> None: ...", "kind": 2, "label": "__setitem__", "sortText": "3.9999.__setitem__"}, {"detail": "def __setstate__[_DTypeT_co: dtype = dtype](\n self: ndarray,\n state: tuple[SupportsIndex, _ShapeLike, _DTypeT_co, numpy.bool, bytes | list[Any]],\n /\n) -> None: ...", "kind": 2, "label": "__setstate__", "sortText": "3.9999.__setstate__"}, {"detail": "\n@overload\ndef __sub__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __sub__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> NoReturn: ...\ndef __sub__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[datetime64]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __sub__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__sub__", "sortText": "3.9999.__sub__"}, {"detail": "\n@overload\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]] | ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _NestedSequence[_SupportsArray[dtype[numpy.bool | integer]]] | _NestedSequence[_SupportsArray[dtype[floating[_64Bit]]]] | _NestedSequence[int] | _SupportsArray[dtype[numpy.bool | integer]] | _SupportsArray[dtype[floating[_64Bit]]] | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[floating]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[complexfloating]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[inexact]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[inexact]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLikeBool_co,\n /\n) -> NoReturn: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __truediv__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__truediv__", "sortText": "3.9999.__truediv__"}, {"detail": "\n@overload\ndef __xor__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __xor__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __xor__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __xor__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __xor__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__xor__", "sortText": "3.9999.__xor__"}]}} {"suite": "data_science", "label": "edit array then complete (edit+completion)", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 10, "character": 31, "iteration": 5, "result": {"isIncomplete": false, "items": [{"detail": "ndarray", "kind": 5, "label": "T", "sortText": "0.9999.T"}, {"detail": "\n@overload\ndef all(\n self : ndarray,\n axis : None = None,\n out : None = None,\n keepdims: Literal[False] = False,\n *,\n where : _ArrayLikeBool_co = True\n) -> numpy.bool: ...\ndef all(\n self : ndarray,\n axis : int | tuple[int, ...],\n out : None = None,\n keepdims: Literal[False] = False,\n *,\n where : _ArrayLikeBool_co = True\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef all(\n self : ndarray,\n axis : int | tuple[int, ...] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n where : _ArrayLikeBool_co = True\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef all[ArrayT: ndarray](\n self : ndarray,\n axis : int | tuple[int, ...] | None = None,\n *,\n out : ArrayT,\n keepdims: builtins.bool = False,\n where : _ArrayLikeBool_co = True\n) -> ArrayT: ...\ndef all[ArrayT: ndarray](\n self : ndarray,\n axis : int | tuple[int, ...] | None,\n out : ArrayT,\n keepdims: builtins.bool = False,\n *,\n where : _ArrayLikeBool_co = True\n) -> ArrayT: ...", "kind": 2, "label": "all", "sortText": "0.9999.all"}, {"detail": "\n@overload\ndef any(\n self : ndarray,\n axis : None = None,\n out : None = None,\n keepdims: Literal[False] = False,\n *,\n where : _ArrayLikeBool_co = True\n) -> numpy.bool: ...\ndef any(\n self : ndarray,\n axis : int | tuple[int, ...],\n out : None = None,\n keepdims: Literal[False] = False,\n *,\n where : _ArrayLikeBool_co = True\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef any(\n self : ndarray,\n axis : int | tuple[int, ...] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n where : _ArrayLikeBool_co = True\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef any[ArrayT: ndarray](\n self : ndarray,\n axis : int | tuple[int, ...] | None = None,\n *,\n out : ArrayT,\n keepdims: builtins.bool = False,\n where : _ArrayLikeBool_co = True\n) -> ArrayT: ...\ndef any[ArrayT: ndarray](\n self : ndarray,\n axis : int | tuple[int, ...] | None,\n out : ArrayT,\n keepdims: builtins.bool = False,\n *,\n where : _ArrayLikeBool_co = True\n) -> ArrayT: ...", "kind": 2, "label": "any", "sortText": "0.9999.any"}, {"detail": "\n@overload\ndef argmax(\n self : ndarray,\n axis : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] = False\n) -> signedinteger[_NBitIntP]: ...\ndef argmax(\n self : ndarray,\n axis : SupportsIndex,\n out : None = None,\n *,\n keepdims: Literal[False] = False\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef argmax(\n self : ndarray,\n axis : SupportsIndex | None = None,\n out : None = None,\n *,\n keepdims: Literal[True]\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef argmax[ArrayT: ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]](\n self : ndarray,\n axis : SupportsIndex | None = None,\n *,\n out : ArrayT,\n keepdims: bool = False\n) -> ArrayT: ...\ndef argmax[ArrayT: ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]](\n self : ndarray,\n axis : SupportsIndex | None,\n out : ArrayT,\n *,\n keepdims: bool = False\n) -> ArrayT: ...", "kind": 2, "label": "argmax", "sortText": "0.9999.argmax"}, {"detail": "\n@overload\ndef argmin(\n self : ndarray,\n axis : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] = False\n) -> signedinteger[_NBitIntP]: ...\ndef argmin(\n self : ndarray,\n axis : SupportsIndex,\n out : None = None,\n *,\n keepdims: Literal[False] = False\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef argmin(\n self : ndarray,\n axis : SupportsIndex | None = None,\n out : None = None,\n *,\n keepdims: Literal[True]\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef argmin[ArrayT: ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]](\n self : ndarray,\n axis : SupportsIndex | None = None,\n *,\n out : ArrayT,\n keepdims: bool = False\n) -> ArrayT: ...\ndef argmin[ArrayT: ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]](\n self : ndarray,\n axis : SupportsIndex | None,\n out : ArrayT,\n *,\n keepdims: bool = False\n) -> ArrayT: ...", "kind": 2, "label": "argmin", "sortText": "0.9999.argmin"}, {"detail": "\n@overload\ndef argpartition(\n self : ndarray,\n kth : _ArrayLikeInt,\n /,\n axis : None,\n kind : Literal['introselect'] = 'introselect',\n order: None = None\n) -> ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]: ...\ndef argpartition(\n self : ndarray,\n kth : _ArrayLikeInt,\n /,\n axis : SupportsIndex = -1,\n kind : Literal['introselect'] = 'introselect',\n order: None = None\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef argpartition(\n self : ndarray[tuple[Any, ...], dtype[void]],\n kth : _ArrayLikeInt,\n /,\n axis : None,\n kind : Literal['introselect'] = 'introselect',\n order: Sequence[str] | str | None = None\n) -> ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]: ...\ndef argpartition(\n self : ndarray[tuple[Any, ...], dtype[void]],\n kth : _ArrayLikeInt,\n /,\n axis : SupportsIndex = -1,\n kind : Literal['introselect'] = 'introselect',\n order: Sequence[str] | str | None = None\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...", "kind": 2, "label": "argpartition", "sortText": "0.9999.argpartition"}, {"detail": "def argsort(\n self : ndarray,\n axis : SupportsIndex | None = ...,\n kind : Literal['H', 'M', 'Q', 'S', 'heap', 'heapsort', 'merge', 'mergesort', 'quick', 'quicksort', 'stable', 'stablesort'] | None = ...,\n order : Sequence[str] | str | None = ...,\n *,\n stable : bool | None = ...,\n descending: bool | None = ...\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...", "kind": 2, "label": "argsort", "sortText": "0.9999.argsort"}, {"detail": "\n@overload\ndef astype[ScalarT: generic](\n self : ndarray,\n dtype : _DTypeLike,\n order : _OrderKACF = ...,\n casting: _CastingKind = ...,\n subok : bool = ...,\n copy : _CopyMode | bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef astype(\n self : ndarray,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None,\n order : _OrderKACF = ...,\n casting: _CastingKind = ...,\n subok : bool = ...,\n copy : _CopyMode | bool = ...\n) -> ndarray: ...", "kind": 2, "label": "astype", "sortText": "0.9999.astype"}, {"detail": "ndarray | None", "kind": 5, "label": "base", "sortText": "0.9999.base"}, {"detail": "def byteswap(\n self : ndarray,\n inplace: bool = ...\n) -> ndarray: ...", "kind": 2, "label": "byteswap", "sortText": "0.9999.byteswap"}, {"detail": "\n@overload\ndef choose(\n self : ndarray,\n /,\n choices: ArrayLike,\n out : None = None,\n mode : _ModeKind = 'raise'\n) -> ndarray: ...\ndef choose[ArrayT: ndarray](\n self : ndarray,\n /,\n choices: ArrayLike,\n out : ArrayT,\n mode : _ModeKind = 'raise'\n) -> ArrayT: ...", "kind": 2, "label": "choose", "sortText": "0.9999.choose"}, {"detail": "\n@overload\ndef clip(\n self : ndarray,\n /,\n min : ArrayLike,\n max : Buffer | _NestedSequence[bytes | complex | str] | _NestedSequence[_SupportsArray[dtype]] | _SupportsArray[dtype] | bytes | complex | str | None = None,\n out : None = None,\n **kwargs: Any\n) -> ndarray: ...\ndef clip(\n self : ndarray,\n /,\n min : None,\n max : ArrayLike,\n out : None = None,\n **kwargs: Any\n) -> ndarray: ...\ndef clip(\n self : ndarray,\n /,\n min : None = None,\n *,\n max : ArrayLike,\n out : None = None,\n **kwargs: Any\n) -> ndarray: ...\ndef clip[ArrayT: ndarray](\n self: ndarray,\n /,\n min: ArrayLike,\n max: Buffer | _NestedSequence[bytes | complex | str] | _NestedSequence[_SupportsArray[dtype]] | _SupportsArray[dtype] | bytes | complex | str | None,\n out: ArrayT,\n **kwargs: Any\n) -> ArrayT: ...\ndef clip[ArrayT: ndarray](\n self : ndarray,\n /,\n min : ArrayLike,\n max : Buffer | _NestedSequence[bytes | complex | str] | _NestedSequence[_SupportsArray[dtype]] | _SupportsArray[dtype] | bytes | complex | str | None = None,\n *,\n out : ArrayT,\n **kwargs: Any\n) -> ArrayT: ...\ndef clip[ArrayT: ndarray](\n self: ndarray,\n /,\n min: None,\n max: ArrayLike,\n out: ArrayT,\n **kwargs: Any\n) -> ArrayT: ...\ndef clip[ArrayT: ndarray](\n self : ndarray,\n /,\n min : None = None,\n *,\n max : ArrayLike,\n out : ArrayT,\n **kwargs: Any\n) -> ArrayT: ...", "kind": 2, "label": "clip", "sortText": "0.9999.clip"}, {"detail": "\n@overload\ndef compress(\n self : ndarray,\n /,\n condition: _ArrayLikeInt_co,\n axis : SupportsIndex | None = None,\n out : None = None\n) -> ndarray: ...\ndef compress[ArrayT: ndarray](\n self: ndarray,\n /,\n condition: _ArrayLikeInt_co,\n axis: SupportsIndex | None,\n out: ArrayT\n) -> ArrayT: ...\ndef compress[ArrayT: ndarray](\n self : ndarray,\n /,\n condition: _ArrayLikeInt_co,\n axis : SupportsIndex | None = None,\n *,\n out : ArrayT\n) -> ArrayT: ...", "kind": 2, "label": "compress", "sortText": "0.9999.compress"}, {"detail": "def conj(self: ndarray) -> ndarray: ...", "kind": 2, "label": "conj", "sortText": "0.9999.conj"}, {"detail": "def conjugate(self: ndarray) -> ndarray: ...", "kind": 2, "label": "conjugate", "sortText": "0.9999.conjugate"}, {"detail": "def copy(\n self : ndarray,\n order: _OrderKACF = ...\n) -> ndarray: ...", "kind": 2, "label": "copy", "sortText": "0.9999.copy"}, {"detail": "_ctypes[int]", "kind": 5, "label": "ctypes", "sortText": "0.9999.ctypes"}, {"detail": "\n@overload\ndef cumprod[DTypeT: dtype[inexact | object_]](\n self : ndarray[Any, DTypeT],\n axis : None = None,\n dtype: None = None,\n out : None = None\n) -> ndarray[tuple[int], DTypeT]: ...\ndef cumprod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : None = None,\n dtype: None = None,\n out : None = None\n) -> ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]: ...\ndef cumprod[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : None = None,\n *,\n dtype: _DTypeLike,\n out : None = None\n) -> ndarray[tuple[int], dtype[ScalarT]]: ...\ndef cumprod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : None = None,\n *,\n dtype: DTypeLike,\n out : None = None\n) -> ndarray[tuple[int]]: ...\ndef cumprod[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : None,\n dtype: _DTypeLike,\n out : None = None\n) -> ndarray[tuple[int], dtype[ScalarT]]: ...\ndef cumprod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : None,\n dtype: DTypeLike,\n out : None = None\n) -> ndarray[tuple[int]]: ...\ndef cumprod[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | object_]]](\n self : ArrayT,\n axis : SupportsIndex,\n dtype: None = None,\n out : None = None\n) -> ArrayT: ...\ndef cumprod[ShapeT: tuple[int, ...]](\n self : ndarray[ShapeT, dtype[numpy.bool | integer]],\n axis : SupportsIndex,\n dtype: None = None,\n out : None = None\n) -> ndarray[ShapeT, dtype[signedinteger[_NBitIntP]]]: ...\ndef cumprod[ShapeT: tuple[int, ...], ScalarT: generic](\n self : ndarray[ShapeT, dtype[numpy.bool | number | object_]],\n axis : SupportsIndex,\n dtype: _DTypeLike,\n out : None = None\n) -> ndarray[ShapeT, dtype[ScalarT]]: ...\ndef cumprod[ShapeT: tuple[int, ...]](\n self : ndarray[ShapeT, dtype[numpy.bool | number | object_]],\n axis : SupportsIndex,\n dtype: DTypeLike,\n out : None = None\n) -> ndarray[ShapeT]: ...\ndef cumprod[ArrayT: ndarray](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis: SupportsIndex | None,\n dtype: _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None,\n out: ArrayT\n) -> ArrayT: ...\ndef cumprod[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : SupportsIndex | None = None,\n dtype: _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT\n) -> ArrayT: ...", "kind": 2, "label": "cumprod", "sortText": "0.9999.cumprod"}, {"detail": "\n@overload\ndef cumsum[DTypeT: dtype[inexact | object_ | timedelta64]](\n self : ndarray[Any, DTypeT],\n axis : None = None,\n dtype: None = None,\n out : None = None\n) -> ndarray[tuple[int], DTypeT]: ...\ndef cumsum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : None = None,\n dtype: None = None,\n out : None = None\n) -> ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]: ...\ndef cumsum[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n *,\n dtype: _DTypeLike,\n out : None = None\n) -> ndarray[tuple[int], dtype[ScalarT]]: ...\ndef cumsum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n *,\n dtype: DTypeLike,\n out : None = None\n) -> ndarray[tuple[int]]: ...\ndef cumsum[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None,\n dtype: _DTypeLike,\n out : None = None\n) -> ndarray[tuple[int], dtype[ScalarT]]: ...\ndef cumsum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None,\n dtype: DTypeLike,\n out : None = None\n) -> ndarray[tuple[int]]: ...\ndef cumsum[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | object_ | timedelta64]]](\n self : ArrayT,\n axis : SupportsIndex,\n dtype: None = None,\n out : None = None\n) -> ArrayT: ...\ndef cumsum[ShapeT: tuple[int, ...]](\n self : ndarray[ShapeT, dtype[numpy.bool | integer]],\n axis : SupportsIndex,\n dtype: None = None,\n out : None = None\n) -> ndarray[ShapeT, dtype[signedinteger[_NBitIntP]]]: ...\ndef cumsum[ShapeT: tuple[int, ...], ScalarT: generic](\n self : ndarray[ShapeT, dtype[numpy.bool | number | object_ | timedelta64]],\n axis : SupportsIndex,\n dtype: _DTypeLike,\n out : None = None\n) -> ndarray[ShapeT, dtype[ScalarT]]: ...\ndef cumsum[ShapeT: tuple[int, ...]](\n self : ndarray[ShapeT, dtype[numpy.bool | number | object_ | timedelta64]],\n axis : SupportsIndex,\n dtype: DTypeLike,\n out : None = None\n) -> ndarray[ShapeT]: ...\ndef cumsum[ArrayT: ndarray](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis: SupportsIndex | None,\n dtype: _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None,\n out: ArrayT\n) -> ArrayT: ...\ndef cumsum[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : SupportsIndex | None = None,\n dtype: _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT\n) -> ArrayT: ...", "kind": 2, "label": "cumsum", "sortText": "0.9999.cumsum"}, {"detail": "memoryview", "kind": 5, "label": "data", "sortText": "0.9999.data"}, {"detail": "Literal['cpu']", "kind": 5, "label": "device", "sortText": "0.9999.device"}, {"detail": "\n@overload\ndef diagonal[DTypeT: dtype](\n self : ndarray[tuple[Never, Never, Never, Never], DTypeT],\n offset: SupportsIndex = 0,\n axis1 : SupportsIndex = 0,\n axis2 : SupportsIndex = 1\n) -> ndarray[tuple[Any, ...], DTypeT]: ...\ndef diagonal[DTypeT: dtype](\n self : ndarray[tuple[int, int], DTypeT],\n offset: SupportsIndex = 0,\n axis1 : SupportsIndex = 0,\n axis2 : SupportsIndex = 1\n) -> ndarray[tuple[int], DTypeT]: ...\ndef diagonal[DTypeT: dtype](\n self : ndarray[tuple[int, int, int], DTypeT],\n offset: SupportsIndex = 0,\n axis1 : SupportsIndex = 0,\n axis2 : SupportsIndex = 1\n) -> ndarray[tuple[int, int], DTypeT]: ...\ndef diagonal(\n self : ndarray,\n offset: SupportsIndex = 0,\n axis1 : SupportsIndex = 0,\n axis2 : SupportsIndex = 1\n) -> ndarray: ...", "kind": 2, "label": "diagonal", "sortText": "0.9999.diagonal"}, {"detail": "\n@overload\ndef dot(\n self: ndarray,\n b : _ScalarLike_co,\n /,\n out : None = None\n) -> ndarray: ...\ndef dot(\n self: ndarray,\n b : ArrayLike,\n /,\n out : None = None\n) -> Any: ...\ndef dot[ArrayT: ndarray](\n self: ndarray,\n b: ArrayLike,\n /,\n out: ArrayT\n) -> ArrayT: ...", "kind": 2, "label": "dot", "sortText": "0.9999.dot"}, {"detail": "dtype", "kind": 5, "label": "dtype", "sortText": "0.9999.dtype"}, {"detail": "def dump(\n self: ndarray,\n file: PathLike[bytes] | PathLike[str] | SupportsWrite[bytes] | bytes | str\n) -> None: ...", "kind": 2, "label": "dump", "sortText": "0.9999.dump"}, {"detail": "def dumps(self: ndarray) -> bytes: ...", "kind": 2, "label": "dumps", "sortText": "0.9999.dumps"}, {"detail": "def fill(\n self: ndarray,\n /,\n value: Any\n) -> None: ...", "kind": 2, "label": "fill", "sortText": "0.9999.fill"}, {"detail": "flagsobj", "kind": 5, "label": "flags", "sortText": "0.9999.flags"}, {"detail": "flatiter", "kind": 5, "label": "flat", "sortText": "0.9999.flat"}, {"detail": "def flatten(\n self : ndarray,\n /,\n order: _OrderKACF = 'C'\n) -> ndarray[tuple[int]]: ...", "kind": 2, "label": "flatten", "sortText": "0.9999.flatten"}, {"detail": "\n@overload\ndef getfield[ScalarT: generic](\n self : ndarray,\n /,\n dtype : _DTypeLike,\n offset: SupportsIndex = 0\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef getfield(\n self : ndarray,\n /,\n dtype : DTypeLike,\n offset: SupportsIndex = 0\n) -> ndarray: ...", "kind": 2, "label": "getfield", "sortText": "0.9999.getfield"}, {"detail": "ndarray", "kind": 5, "label": "imag", "sortText": "0.9999.imag"}, {"detail": "\n@overload\ndef item[T](\n self : ndarray[tuple[Any, ...], dtype[generic[T]]],\n i0 : SupportsIndex | tuple[SupportsIndex, ...] = ...,\n /,\n *args: SupportsIndex\n) -> T: ...\ndef item(\n self : ndarray[Any, StringDType],\n arg0 : SupportsIndex | tuple[SupportsIndex, ...] = ...,\n /,\n *args: SupportsIndex\n) -> str: ...", "kind": 2, "label": "item", "sortText": "0.9999.item"}, {"detail": "int", "kind": 5, "label": "itemsize", "sortText": "0.9999.itemsize"}, {"detail": "ndarray", "kind": 5, "label": "mT", "sortText": "0.9999.mT"}, {"detail": "\n@overload\ndef max[ScalarT: numpy.bool | datetime64 | number | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef max[ScalarT: numpy.bool | datetime64 | number | object_ | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef max[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool | datetime64 | number | object_ | timedelta64]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef max(\n self : ndarray[tuple[Any, ...], dtype[object_]],\n axis : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef max[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | datetime64 | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n *,\n out : ArrayT,\n keepdims: _NoValueType | builtins.bool = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...", "kind": 2, "label": "max", "sortText": "0.9999.max"}, {"detail": "\n@overload\ndef mean(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer | object_]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> float64: ...\ndef mean(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef mean(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef mean[ScalarT: inexact | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef mean[ScalarT: inexact | object_ | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef mean[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | object_ | timedelta64]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef mean[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef mean[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n keepdims: Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef mean[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None,\n dtype : _DTypeLike,\n out : None = None,\n *,\n keepdims: Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef mean[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _DTypeLike,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef mean[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT,\n keepdims: _NoValueType | builtins.bool = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef mean(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef mean(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray: ...\ndef mean(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray: ...", "kind": 2, "label": "mean", "sortText": "0.9999.mean"}, {"detail": "\n@overload\ndef min[ScalarT: numpy.bool | datetime64 | number | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef min[ScalarT: numpy.bool | datetime64 | number | object_ | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef min[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool | datetime64 | number | object_ | timedelta64]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef min(\n self : ndarray[tuple[Any, ...], dtype[object_]],\n axis : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef min[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | datetime64 | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n *,\n out : ArrayT,\n keepdims: _NoValueType | builtins.bool = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...", "kind": 2, "label": "min", "sortText": "0.9999.min"}, {"detail": "int", "kind": 5, "label": "nbytes", "sortText": "0.9999.nbytes"}, {"detail": "int", "kind": 5, "label": "ndim", "sortText": "0.9999.ndim"}, {"detail": "\n@overload\ndef nonzero(self: ndarray[tuple[Never, Never, Never, Never]]) -> tuple[ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]], ...]: ...\ndef nonzero(self: ndarray[tuple[int]]) -> tuple[ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]]: ...\ndef nonzero(self: ndarray[tuple[int, int]]) -> tuple[ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]], ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]]: ...\ndef nonzero(self: ndarray[tuple[int, int, int]]) -> tuple[ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]], ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]], ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]]]: ...\ndef nonzero(self: ndarray) -> tuple[ndarray[tuple[int], dtype[signedinteger[_NBitIntP]]], ...]: ...", "kind": 2, "label": "nonzero", "sortText": "0.9999.nonzero"}, {"detail": "\n@overload\ndef partition(\n self : ndarray,\n kth : _ArrayLikeInt,\n /,\n axis : SupportsIndex = -1,\n kind : Literal['introselect'] = 'introselect',\n order: None = None\n) -> None: ...\ndef partition(\n self : ndarray[tuple[Any, ...], dtype[void]],\n kth : _ArrayLikeInt,\n /,\n axis : SupportsIndex = -1,\n kind : Literal['introselect'] = 'introselect',\n order: Sequence[str] | str | None = None\n) -> None: ...", "kind": 2, "label": "partition", "sortText": "0.9999.partition"}, {"detail": "\n@overload\ndef prod[ScalarT: inexact](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef prod[ScalarT: inexact | object_](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef prod[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | object_]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | int | integer = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> signedinteger[_NBitIntP]: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | int | integer = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | int | integer = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[object_]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef prod[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef prod[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...] | None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef prod[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...] | None,\n dtype : _DTypeLike,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef prod[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...],\n dtype : _DTypeLike,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef prod[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT,\n keepdims: _NoValueType | builtins.bool = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef prod[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...] | None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None,\n out : ArrayT,\n *,\n keepdims: _NoValueType | builtins.bool = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...],\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray: ...\ndef prod(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray: ...", "kind": 2, "label": "prod", "sortText": "0.9999.prod"}, {"detail": "def put(\n self : ndarray,\n indices: _ArrayLikeInt_co,\n values : ArrayLike,\n /,\n mode : _ModeKind = 'raise'\n) -> None: ...", "kind": 2, "label": "put", "sortText": "0.9999.put"}, {"detail": "def ravel(\n self : ndarray,\n /,\n order: _OrderKACF = 'C'\n) -> ndarray[tuple[int]]: ...", "kind": 2, "label": "ravel", "sortText": "0.9999.ravel"}, {"detail": "ndarray", "kind": 5, "label": "real", "sortText": "0.9999.real"}, {"detail": "\n@overload\ndef repeat(\n self : ndarray,\n repeats: _ArrayLikeInt_co,\n /,\n axis : None = None\n) -> ndarray[tuple[int]]: ...\ndef repeat(\n self: ndarray,\n repeats: _ArrayLikeInt_co,\n /,\n axis: SupportsIndex\n) -> ndarray: ...", "kind": 2, "label": "repeat", "sortText": "0.9999.repeat"}, {"detail": "\n@overload\ndef reshape(\n self : ndarray,\n shape: None,\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray: ...\ndef reshape(\n self : ndarray,\n shape: Sequence[Never],\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray[tuple[()]]: ...\ndef reshape[AnyShapeT: (tuple[()], tuple[int], tuple[int, int], tuple[int, int, int], tuple[int, int, int, int], tuple[int, int, int, int, int], tuple[int, int, int, int, int, int], tuple[int, int, int, int, int, int, int], tuple[int, int, int, int, int, int, int, int])](\n self : ndarray,\n shape: AnyShapeT,\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray[AnyShapeT]: ...\ndef reshape(\n self : ndarray,\n size1: SupportsIndex,\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray[tuple[int]]: ...\ndef reshape(\n self : ndarray,\n size1: SupportsIndex,\n size2: SupportsIndex,\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray[tuple[int, int]]: ...\ndef reshape(\n self : ndarray,\n size1: SupportsIndex,\n size2: SupportsIndex,\n size3: SupportsIndex,\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray[tuple[int, int, int]]: ...\ndef reshape(\n self : ndarray,\n size1: SupportsIndex,\n size2: SupportsIndex,\n size3: SupportsIndex,\n size4: SupportsIndex,\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray[tuple[int, int, int, int]]: ...\ndef reshape(\n self : ndarray,\n size0 : SupportsIndex,\n /,\n *shape: SupportsIndex,\n *,\n order : _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray: ...\ndef reshape(\n self : ndarray,\n shape: Sequence[SupportsIndex],\n /,\n *,\n order: _OrderACF = 'C',\n copy : bool | None = None\n) -> ndarray: ...", "kind": 2, "label": "reshape", "sortText": "0.9999.reshape"}, {"detail": "\n@overload\ndef resize(\n self : ndarray,\n new_shape: _ShapeLike,\n /,\n *,\n refcheck : bool = True\n) -> None: ...\ndef resize(\n self : ndarray,\n /,\n *new_shape: SupportsIndex,\n *,\n refcheck : bool = True\n) -> None: ...", "kind": 2, "label": "resize", "sortText": "0.9999.resize"}, {"detail": "\n@overload\ndef round(\n self : ndarray,\n /,\n decimals: SupportsIndex = 0,\n out : None = None\n) -> ndarray: ...\ndef round[ArrayT: ndarray](\n self: ndarray,\n /,\n decimals: SupportsIndex,\n out: ArrayT\n) -> ArrayT: ...\ndef round[ArrayT: ndarray](\n self : ndarray,\n /,\n decimals: SupportsIndex = 0,\n *,\n out : ArrayT\n) -> ArrayT: ...", "kind": 2, "label": "round", "sortText": "0.9999.round"}, {"detail": "\n@overload\ndef searchsorted(\n self : ndarray,\n v : _ScalarLike_co,\n /,\n side : _SortSide = 'left',\n sorter: _NestedSequence[_SupportsArray[dtype[numpy.bool | integer]]] | _NestedSequence[int] | _SupportsArray[dtype[numpy.bool | integer]] | int | None = None\n) -> signedinteger[_NBitIntP]: ...\ndef searchsorted(\n self : ndarray,\n v : ArrayLike,\n /,\n side : _SortSide = 'left',\n sorter: _NestedSequence[_SupportsArray[dtype[numpy.bool | integer]]] | _NestedSequence[int] | _SupportsArray[dtype[numpy.bool | integer]] | int | None = None\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...", "kind": 2, "label": "searchsorted", "sortText": "0.9999.searchsorted"}, {"detail": "def setfield(\n self : ndarray,\n val : ArrayLike,\n /,\n dtype : DTypeLike,\n offset: SupportsIndex = 0\n) -> None: ...", "kind": 2, "label": "setfield", "sortText": "0.9999.setfield"}, {"detail": "def setflags(\n self : ndarray,\n /,\n *,\n write: bool | None = None,\n align: bool | None = None,\n uic : bool | None = None\n) -> None: ...", "kind": 2, "label": "setflags", "sortText": "0.9999.setflags"}, {"detail": "tuple[Any, ...]", "kind": 5, "label": "shape", "sortText": "0.9999.shape"}, {"detail": "int", "kind": 5, "label": "size", "sortText": "0.9999.size"}, {"detail": "def sort(\n self : ndarray,\n /,\n axis : SupportsIndex = -1,\n kind : Literal['H', 'M', 'Q', 'S', 'heap', 'heapsort', 'merge', 'mergesort', 'quick', 'quicksort', 'stable', 'stablesort'] | None = None,\n order : Sequence[str] | str | None = None,\n *,\n stable : bool | None = None,\n descending: bool | None = None\n) -> None: ...", "kind": 2, "label": "sort", "sortText": "0.9999.sort"}, {"detail": "def squeeze(\n self: ndarray,\n /,\n axis: SupportsIndex | tuple[SupportsIndex, ...] | None = ...\n) -> ndarray: ...", "kind": 2, "label": "squeeze", "sortText": "0.9999.squeeze"}, {"detail": "\n@overload\ndef std(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | complex128 | integer | object_]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> float64: ...\ndef std(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | complex128 | integer]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef std(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | complex128 | integer]],\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef std[ScalarT: floating | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ScalarT: ...\ndef std[ScalarT: floating | object_ | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef std[ArrayT: ndarray[tuple[Any, ...], dtype[floating | object_ | timedelta64]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ArrayT: ...\ndef std[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ScalarT: ...\ndef std[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef std[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None,\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef std[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef std[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT,\n ddof : float = 0,\n keepdims : _NoValueType | builtins.bool = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ArrayT: ...\ndef std(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> Any: ...\ndef std(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray: ...\ndef std(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray: ...", "kind": 2, "label": "std", "sortText": "0.9999.std"}, {"detail": "tuple[int, ...]", "kind": 5, "label": "strides", "sortText": "0.9999.strides"}, {"detail": "\n@overload\ndef sum[ScalarT: inexact | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef sum[ScalarT: inexact | object_ | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef sum[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | object_ | timedelta64]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | int | integer = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> signedinteger[_NBitIntP]: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | int | integer = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | int | integer = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_NBitIntP]]]: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[object_]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef sum[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ScalarT: ...\ndef sum[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef sum[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None,\n dtype : _DTypeLike,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef sum[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _DTypeLike,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef sum[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT,\n keepdims: _NoValueType | builtins.bool = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef sum[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None,\n out : ArrayT,\n *,\n keepdims: _NoValueType | builtins.bool = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ArrayT: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> Any: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[False] | _NoValueType = ...,\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray: ...\ndef sum(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n *,\n keepdims: Literal[True],\n initial : _NoValueType | numpy.bool | complex | number = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...\n) -> ndarray: ...", "kind": 2, "label": "sum", "sortText": "0.9999.sum"}, {"detail": "def swapaxes(\n self: ndarray,\n axis1: SupportsIndex,\n axis2: SupportsIndex,\n /\n) -> ndarray: ...", "kind": 2, "label": "swapaxes", "sortText": "0.9999.swapaxes"}, {"detail": "\n@overload\ndef take[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n indices: _IntLike_co,\n /,\n axis : SupportsIndex | None = ...,\n out : None = None,\n mode : _ModeKind = ...\n) -> ScalarT: ...\ndef take(\n self : ndarray,\n indices: _ArrayLikeInt_co,\n /,\n axis : SupportsIndex | None = ...,\n out : None = None,\n mode : _ModeKind = ...\n) -> ndarray: ...\ndef take[ArrayT: ndarray](\n self : ndarray,\n indices: _ArrayLikeInt_co,\n /,\n axis : SupportsIndex | None = ...,\n *,\n out : ArrayT,\n mode : _ModeKind = ...\n) -> ArrayT: ...\ndef take[ArrayT: ndarray](\n self : ndarray,\n indices: _ArrayLikeInt_co,\n /,\n axis : SupportsIndex | None,\n out : ArrayT,\n mode : _ModeKind = ...\n) -> ArrayT: ...", "kind": 2, "label": "take", "sortText": "0.9999.take"}, {"detail": "def to_device(\n self : ndarray,\n device: Literal['cpu'],\n /,\n *,\n stream: int | Any | None = ...\n) -> ndarray: ...", "kind": 2, "label": "to_device", "sortText": "0.9999.to_device"}, {"detail": "def tobytes(\n self : ndarray,\n order: _OrderKACF = ...\n) -> bytes: ...", "kind": 2, "label": "tobytes", "sortText": "0.9999.tobytes"}, {"detail": "def tofile(\n self : ndarray,\n fid : PathLike[bytes] | PathLike[str] | _SupportsFileMethods | bytes | str,\n /,\n sep : str = '',\n format: str = '%s'\n) -> None: ...", "kind": 2, "label": "tofile", "sortText": "0.9999.tofile"}, {"detail": "\n@overload\ndef tolist[T](self: ndarray[tuple[Never], dtype[generic[T]]], /) -> Any: ...\ndef tolist[T](self: ndarray[tuple[()], dtype[generic[T]]], /) -> T: ...\ndef tolist[T](self: ndarray[tuple[int], dtype[generic[T]]], /) -> list[T]: ...\ndef tolist[T](self: ndarray[tuple[int, int], dtype[generic[T]]], /) -> list[list[T]]: ...\ndef tolist[T](self: ndarray[tuple[int, int, int], dtype[generic[T]]], /) -> list[list[list[T]]]: ...\ndef tolist(self: ndarray, /) -> Any: ...", "kind": 2, "label": "tolist", "sortText": "0.9999.tolist"}, {"detail": "\n@overload\ndef trace(\n self : ndarray,\n /,\n offset: SupportsIndex = 0,\n axis1 : SupportsIndex = 0,\n axis2 : SupportsIndex = 1,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None\n) -> Any: ...\ndef trace[ArrayT: ndarray](\n self : ndarray,\n /,\n offset: SupportsIndex = 0,\n axis1 : SupportsIndex = 0,\n axis2 : SupportsIndex = 1,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT\n) -> ArrayT: ...\ndef trace[ArrayT: ndarray](\n self: ndarray,\n /,\n offset: SupportsIndex,\n axis1: SupportsIndex,\n axis2: SupportsIndex,\n dtype: _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None,\n out: ArrayT\n) -> ArrayT: ...", "kind": 2, "label": "trace", "sortText": "0.9999.trace"}, {"detail": "\n@overload\ndef transpose(\n self: ndarray,\n axes: Sequence[SupportsIndex] | SupportsIndex | None,\n /\n) -> ndarray: ...\ndef transpose(\n self: ndarray,\n /,\n *axes: SupportsIndex\n) -> ndarray: ...", "kind": 2, "label": "transpose", "sortText": "0.9999.transpose"}, {"detail": "\n@overload\ndef var(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | complex128 | integer | object_]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> float64: ...\ndef var(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | complex128 | integer]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef var(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | complex128 | integer]],\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef var[ScalarT: floating | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ScalarT: ...\ndef var[ScalarT: floating | object_ | timedelta64](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n axis : int | tuple[int, ...],\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef var[ArrayT: ndarray[tuple[Any, ...], dtype[floating | object_ | timedelta64]]](\n self : ArrayT,\n axis : int | tuple[int, ...] | None = None,\n dtype : None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ArrayT: ...\ndef var[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ScalarT: ...\ndef var[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n *,\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef var[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None,\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef var[ScalarT: generic](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _DTypeLike,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef var[ArrayT: ndarray](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n out : ArrayT,\n ddof : float = 0,\n keepdims : _NoValueType | builtins.bool = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ArrayT: ...\ndef var(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> Any: ...\ndef var(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...],\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[False] | _NoValueType = ...,\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray: ...\ndef var(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_ | timedelta64]],\n axis : int | tuple[int, ...] | None = None,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n out : None = None,\n ddof : float = 0,\n *,\n keepdims : Literal[True],\n where : _NestedSequence[_SupportsArray[dtype[numpy.bool]]] | _NestedSequence[builtins.bool] | _NoValueType | _SupportsArray[dtype[numpy.bool]] | builtins.bool = ...,\n mean : _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _NoValueType | _SupportsArray[dtype[numpy.bool | number]] | complex = ...,\n correction: _NoValueType | float = ...\n) -> ndarray: ...", "kind": 2, "label": "var", "sortText": "0.9999.var"}, {"detail": "\n@overload\ndef view(self: ndarray, /) -> ndarray: ...\ndef view[DTypeT: dtype](\n self: ndarray,\n /,\n dtype: numpy._HasDType[DTypeT] | DTypeT\n) -> ndarray[tuple[Any, ...], DTypeT]: ...\ndef view[ScalarT: generic](\n self: ndarray,\n /,\n dtype: _DTypeLike\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef view[ArrayT: ndarray](\n self: ndarray,\n /,\n *,\n type: type[ArrayT]\n) -> ArrayT: ...\ndef view[ArrayT: ndarray](\n self: ndarray,\n /,\n dtype: type[ArrayT]\n) -> ArrayT: ...\ndef view(\n self: ndarray,\n /,\n dtype: DTypeLike\n) -> ndarray: ...\ndef view[ArrayT: ndarray](\n self: ndarray,\n /,\n dtype: DTypeLike,\n type: type[ArrayT]\n) -> ArrayT: ...", "kind": 2, "label": "view", "sortText": "0.9999.view"}, {"detail": "\n@overload\ndef __abs__[ShapeT: tuple[int, ...], NBitT: NBitBase](self: ndarray[ShapeT, dtype[complexfloating[NBitT, NBitT]]], /) -> ndarray[ShapeT, dtype[floating[NBitT]]]: ...\ndef __abs__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer | object_ | timedelta64]]](self: ArrayT, /) -> ArrayT: ...", "kind": 2, "label": "__abs__", "sortText": "3.9999.__abs__"}, {"detail": "\n@overload\ndef __add__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __add__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __add__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[datetime64]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[datetime64]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[bytes_]],\n other: _ArrayLikeBytes_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[bytes_]]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[str_]],\n other: _ArrayLikeStr_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[str_]]: ...\ndef __add__(\n self: ndarray[Any, StringDType],\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ndarray[tuple[Any, ...], StringDType]: ...\ndef __add__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __add__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__add__", "sortText": "3.9999.__add__"}, {"detail": "\n@overload\ndef __and__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __and__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __and__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __and__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __and__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__and__", "sortText": "3.9999.__and__"}, {"detail": "\n@overload\ndef __array__(\n self : ndarray,\n dtype: None = None,\n /,\n *,\n copy : bool | None = None\n) -> ndarray: ...\ndef __array__[DTypeT: dtype](\n self : ndarray,\n dtype: DTypeT,\n /,\n *,\n copy : bool | None = None\n) -> ndarray[tuple[Any, ...], DTypeT]: ...", "kind": 2, "label": "__array__", "sortText": "3.9999.__array__"}, {"detail": "def __array_finalize__(\n self: ndarray,\n obj: ndarray | None,\n /\n) -> None: ...", "kind": 2, "label": "__array_finalize__", "sortText": "3.9999.__array_finalize__"}, {"detail": "def __array_function__(\n self: ndarray,\n func: (...) -> Any,\n types: Iterable[type[Any]],\n args: Iterable[Any],\n kwargs: Mapping[str, Any]\n) -> Any: ...", "kind": 2, "label": "__array_function__", "sortText": "3.9999.__array_function__"}, {"detail": "dict[str, Any]", "kind": 5, "label": "__array_interface__", "sortText": "3.9999.__array_interface__"}, {"detail": "def __array_namespace__(\n self : ndarray,\n /,\n *,\n api_version: Literal['2021.12', '2022.12', '2023.12', '2024.12', '2025.12'] | None = None\n) -> ModuleType: ...", "kind": 2, "label": "__array_namespace__", "sortText": "3.9999.__array_namespace__"}, {"detail": "float", "kind": 5, "label": "__array_priority__", "sortText": "3.9999.__array_priority__"}, {"detail": "CapsuleType", "kind": 5, "label": "__array_struct__", "sortText": "3.9999.__array_struct__"}, {"detail": "def __array_ufunc__(\n self: ndarray,\n ufunc: ufunc,\n method: Literal['__call__', 'accumulate', 'at', 'outer', 'reduce', 'reduceat'],\n /,\n *inputs: Any,\n **kwargs: Any\n) -> Any: ...", "kind": 2, "label": "__array_ufunc__", "sortText": "3.9999.__array_ufunc__"}, {"detail": "def __array_wrap__[ShapeT: tuple[int, ...], DTypeT: dtype](\n self : ndarray,\n array : ndarray[ShapeT, DTypeT],\n context : tuple[ufunc, tuple[Any, ...], int] | None = ...,\n return_scalar: bool = ...,\n /\n) -> ndarray[ShapeT, DTypeT]: ...", "kind": 2, "label": "__array_wrap__", "sortText": "3.9999.__array_wrap__"}, {"detail": "def __bool__(self: ndarray, /) -> bool: ...", "kind": 2, "label": "__bool__", "sortText": "3.9999.__bool__"}, {"detail": "def __buffer__(\n self: ndarray,\n flags: int,\n /\n) -> memoryview: ...", "kind": 2, "label": "__buffer__", "sortText": "3.9999.__buffer__"}, {"detail": "def __class_getitem__(\n cls: type[ndarray],\n item: Any,\n /\n) -> GenericAlias: ...", "kind": 2, "label": "__class_getitem__", "sortText": "3.9999.__class_getitem__"}, {"detail": "def __complex__(self: ndarray[tuple[Any, ...], dtype[numpy.bool | number | object_]], /) -> complex: ...", "kind": 2, "label": "__complex__", "sortText": "3.9999.__complex__"}, {"detail": "def __contains__(\n self: ndarray,\n value: object,\n /\n) -> bool: ...", "kind": 2, "label": "__contains__", "sortText": "3.9999.__contains__"}, {"detail": "def __copy__(self: ndarray) -> ndarray: ...", "kind": 2, "label": "__copy__", "sortText": "3.9999.__copy__"}, {"detail": "def __deepcopy__(\n self: ndarray,\n memo: dict[int, Any] | None,\n /\n) -> ndarray: ...", "kind": 2, "label": "__deepcopy__", "sortText": "3.9999.__deepcopy__"}, {"detail": "\n@overload\ndef __divmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n rhs: numpy.bool | int,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[ScalarT]], ndarray[tuple[Any, ...], dtype[ScalarT]]]: ...\ndef __divmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n rhs: _ArrayLikeBool_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[ScalarT]], ndarray[tuple[Any, ...], dtype[ScalarT]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n rhs: _ArrayLikeBool_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]]: ...\ndef __divmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n rhs: _ArrayLike,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[ScalarT]], ndarray[tuple[Any, ...], dtype[ScalarT]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n rhs: _ArrayLikeFloat64_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n rhs: _ArrayLike,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n rhs: _ArrayLikeUInt_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[unsignedinteger]], ndarray[tuple[Any, ...], dtype[unsignedinteger]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n rhs: _ArrayLikeInt_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[signedinteger]], ndarray[tuple[Any, ...], dtype[signedinteger]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n rhs: _ArrayLikeFloat_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[floating]], ndarray[tuple[Any, ...], dtype[floating]]]: ...\ndef __divmod__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n rhs: _ArrayLike,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[signedinteger[_64Bit]]], ndarray[tuple[Any, ...], dtype[timedelta64]]]: ...", "kind": 2, "label": "__divmod__", "sortText": "3.9999.__divmod__"}, {"detail": "def __dlpack__(\n self : ndarray[tuple[Any, ...], dtype[number]],\n /,\n *,\n stream : int | Any | None = None,\n max_version: tuple[int, int] | None = None,\n dl_device : tuple[int, int] | None = None,\n copy : bool | None = None\n) -> CapsuleType: ...", "kind": 2, "label": "__dlpack__", "sortText": "3.9999.__dlpack__"}, {"detail": "def __dlpack_device__(self: ndarray, /) -> tuple[Literal[1], Literal[0]]: ...", "kind": 2, "label": "__dlpack_device__", "sortText": "3.9999.__dlpack_device__"}, {"detail": "def __eq__(\n self: ndarray,\n other: Any,\n /\n) -> Any: ...", "kind": 2, "label": "__eq__", "sortText": "3.9999.__eq__"}, {"detail": "def __float__(self: ndarray, /) -> float: ...", "kind": 2, "label": "__float__", "sortText": "3.9999.__float__"}, {"detail": "\n@overload\ndef __floordiv__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __floordiv__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __floordiv__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_64Bit]]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLikeBool_co,\n /\n) -> NoReturn: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __floordiv__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __floordiv__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__floordiv__", "sortText": "3.9999.__floordiv__"}, {"detail": "\n@overload\ndef __ge__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ge__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ge__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ge__(\n self: ndarray[tuple[Any, ...], dtype[bytes_]],\n other: _ArrayLikeBytes_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ge__(\n self: ndarray[tuple[Any, ...], StringDType | dtype[str_]],\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ge__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: object,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ge__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...", "kind": 2, "label": "__ge__", "sortText": "3.9999.__ge__"}, {"detail": "\n@overload\ndef __getitem__(\n self: ndarray,\n key: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]] | tuple[ndarray[tuple[Any, ...], dtype[numpy.bool | integer]], ...],\n /\n) -> ndarray: ...\ndef __getitem__(\n self: ndarray,\n key: SupportsIndex | tuple[SupportsIndex, ...],\n /\n) -> Any: ...\ndef __getitem__(\n self: ndarray,\n key: _ToIndices,\n /\n) -> ndarray: ...\ndef __getitem__(\n self: ndarray[tuple[Any, ...], dtype[void]],\n key: str,\n /\n) -> ndarray: ...\ndef __getitem__(\n self: ndarray[tuple[Any, ...], dtype[void]],\n key: list[str],\n /\n) -> ndarray[tuple[Any, ...], dtype[void]]: ...", "kind": 2, "label": "__getitem__", "sortText": "3.9999.__getitem__"}, {"detail": "\n@overload\ndef __gt__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __gt__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __gt__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __gt__(\n self: ndarray[tuple[Any, ...], dtype[bytes_]],\n other: _ArrayLikeBytes_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __gt__(\n self: ndarray[tuple[Any, ...], StringDType | dtype[str_]],\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __gt__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: object,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __gt__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...", "kind": 2, "label": "__gt__", "sortText": "3.9999.__gt__"}, {"detail": "None", "kind": 5, "label": "__hash__", "sortText": "3.9999.__hash__"}, {"detail": "\n@overload\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool]]](\n self: ArrayT,\n other: _ArrayLikeBool_co,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[complexfloating]]](\n self: ArrayT,\n other: _ArrayLikeComplex_co,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[inexact]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[number]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[datetime64 | timedelta64]]](\n self: ArrayT,\n other: _ArrayLikeTD64_co,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[bytes_]]](\n self: ArrayT,\n other: _ArrayLikeBytes_co,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], StringDType | dtype[str_]]](\n self: ArrayT,\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ArrayT: ...\ndef __iadd__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__iadd__", "sortText": "3.9999.__iadd__"}, {"detail": "\n@overload\ndef __iand__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool]]](\n self: ArrayT,\n other: _ArrayLikeBool_co,\n /\n) -> ArrayT: ...\ndef __iand__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __iand__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__iand__", "sortText": "3.9999.__iand__"}, {"detail": "\n@overload\ndef __ifloordiv__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __ifloordiv__[ArrayT: ndarray[tuple[Any, ...], dtype[floating | timedelta64]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __ifloordiv__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__ifloordiv__", "sortText": "3.9999.__ifloordiv__"}, {"detail": "\n@overload\ndef __ilshift__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __ilshift__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__ilshift__", "sortText": "3.9999.__ilshift__"}, {"detail": "\n@overload\ndef __imatmul__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool]]](\n self: ArrayT,\n other: _ArrayLikeBool_co,\n /\n) -> ArrayT: ...\ndef __imatmul__[ArrayT: ndarray[tuple[Any, ...], dtype[complexfloating]]](\n self: ArrayT,\n other: _ArrayLikeComplex_co,\n /\n) -> ArrayT: ...\ndef __imatmul__[ArrayT: ndarray[tuple[Any, ...], dtype[inexact]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __imatmul__[ArrayT: ndarray[tuple[Any, ...], dtype[number]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __imatmul__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__imatmul__", "sortText": "3.9999.__imatmul__"}, {"detail": "\n@overload\ndef __imod__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __imod__[ArrayT: ndarray[tuple[Any, ...], dtype[floating]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __imod__[ArrayT: ndarray[tuple[Any, ...], dtype[timedelta64]]](\n self: ArrayT,\n other: _ArrayLike,\n /\n) -> ArrayT: ...\ndef __imod__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__imod__", "sortText": "3.9999.__imod__"}, {"detail": "\n@overload\ndef __imul__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool]]](\n self: ArrayT,\n other: _ArrayLikeBool_co,\n /\n) -> ArrayT: ...\ndef __imul__[ArrayT: ndarray[tuple[Any, ...], dtype[complexfloating]]](\n self: ArrayT,\n other: _ArrayLikeComplex_co,\n /\n) -> ArrayT: ...\ndef __imul__[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | timedelta64]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __imul__[ArrayT: ndarray[tuple[Any, ...], dtype[character | number]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __imul__[ArrayT: ndarray[tuple[Any, ...], StringDType | dtype[str_]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __imul__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__imul__", "sortText": "3.9999.__imul__"}, {"detail": "def __index__(self: ndarray[tuple[Any, ...], dtype[integer]], /) -> int: ...", "kind": 2, "label": "__index__", "sortText": "3.9999.__index__"}, {"detail": "def __int__(self: ndarray, /) -> int: ...", "kind": 2, "label": "__int__", "sortText": "3.9999.__int__"}, {"detail": "def __invert__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | object_]]](self: ArrayT, /) -> ArrayT: ...", "kind": 2, "label": "__invert__", "sortText": "3.9999.__invert__"}, {"detail": "\n@overload\ndef __ior__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool]]](\n self: ArrayT,\n other: _ArrayLikeBool_co,\n /\n) -> ArrayT: ...\ndef __ior__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __ior__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__ior__", "sortText": "3.9999.__ior__"}, {"detail": "\n@overload\ndef __ipow__[ArrayT: ndarray[tuple[Any, ...], dtype[complexfloating]]](\n self: ArrayT,\n other: _ArrayLikeComplex_co,\n /\n) -> ArrayT: ...\ndef __ipow__[ArrayT: ndarray[tuple[Any, ...], dtype[inexact]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __ipow__[ArrayT: ndarray[tuple[Any, ...], dtype[number]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __ipow__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__ipow__", "sortText": "3.9999.__ipow__"}, {"detail": "\n@overload\ndef __irshift__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __irshift__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__irshift__", "sortText": "3.9999.__irshift__"}, {"detail": "\n@overload\ndef __isub__[ArrayT: ndarray[tuple[Any, ...], dtype[complexfloating]]](\n self: ArrayT,\n other: _ArrayLikeComplex_co,\n /\n) -> ArrayT: ...\ndef __isub__[ArrayT: ndarray[tuple[Any, ...], dtype[inexact]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __isub__[ArrayT: ndarray[tuple[Any, ...], dtype[number]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __isub__[ArrayT: ndarray[tuple[Any, ...], dtype[datetime64 | timedelta64]]](\n self: ArrayT,\n other: _ArrayLikeTD64_co,\n /\n) -> ArrayT: ...\ndef __isub__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__isub__", "sortText": "3.9999.__isub__"}, {"detail": "\n@overload\ndef __iter__(self: ndarray[tuple[Never, Never, Never, Never, Never, Never, Never, Never, Never], Any], /) -> Iterator[Any]: ...\ndef __iter__[ScalarT: _ScalarNotObject](self: ndarray[tuple[int], dtype[ScalarT]], /) -> Iterator[ScalarT]: ...\ndef __iter__(self: ndarray[tuple[int], StringDType], /) -> Iterator[str]: ...\ndef __iter__[DTypeT: dtype](self: ndarray[tuple[int, int, *tuple[int, ...]], DTypeT], /) -> Iterator[ndarray[tuple[Any, ...], DTypeT]]: ...\ndef __iter__(self: ndarray, /) -> Iterator[Any]: ...", "kind": 2, "label": "__iter__", "sortText": "3.9999.__iter__"}, {"detail": "\n@overload\ndef __itruediv__[ArrayT: ndarray[tuple[Any, ...], dtype[complexfloating]]](\n self: ArrayT,\n other: _ArrayLikeComplex_co,\n /\n) -> ArrayT: ...\ndef __itruediv__[ArrayT: ndarray[tuple[Any, ...], dtype[inexact | timedelta64]]](\n self: ArrayT,\n other: _ArrayLikeFloat_co,\n /\n) -> ArrayT: ...\ndef __itruediv__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__itruediv__", "sortText": "3.9999.__itruediv__"}, {"detail": "\n@overload\ndef __ixor__[ArrayT: ndarray[tuple[Any, ...], dtype[numpy.bool]]](\n self: ArrayT,\n other: _ArrayLikeBool_co,\n /\n) -> ArrayT: ...\ndef __ixor__[ArrayT: ndarray[tuple[Any, ...], dtype[integer]]](\n self: ArrayT,\n other: _ArrayLikeInt_co,\n /\n) -> ArrayT: ...\ndef __ixor__[ArrayT: ndarray[tuple[Any, ...], dtype[object_]]](\n self: ArrayT,\n other: object,\n /\n) -> ArrayT: ...", "kind": 2, "label": "__ixor__", "sortText": "3.9999.__ixor__"}, {"detail": "\n@overload\ndef __le__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __le__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __le__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __le__(\n self: ndarray[tuple[Any, ...], dtype[bytes_]],\n other: _ArrayLikeBytes_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __le__(\n self: ndarray[tuple[Any, ...], StringDType | dtype[str_]],\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __le__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: object,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __le__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...", "kind": 2, "label": "__le__", "sortText": "3.9999.__le__"}, {"detail": "def __len__(self: ndarray) -> int: ...", "kind": 2, "label": "__len__", "sortText": "3.9999.__len__"}, {"detail": "\n@overload\ndef __lshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __lshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __lshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __lshift__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __lshift__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__lshift__", "sortText": "3.9999.__lshift__"}, {"detail": "\n@overload\ndef __lt__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __lt__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __lt__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __lt__(\n self: ndarray[tuple[Any, ...], dtype[bytes_]],\n other: _ArrayLikeBytes_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __lt__(\n self: ndarray[tuple[Any, ...], StringDType | dtype[str_]],\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __lt__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: object,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __lt__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...", "kind": 2, "label": "__lt__", "sortText": "3.9999.__lt__"}, {"detail": "\n@overload\ndef __matmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __matmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[floating[_64Bit]]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[complexfloating[_64Bit, _64Bit]]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __matmul__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __matmul__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__matmul__", "sortText": "3.9999.__matmul__"}, {"detail": "\n@overload\ndef __mod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __mod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __mod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __mod__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __mod__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__mod__", "sortText": "3.9999.__mod__"}, {"detail": "\n@overload\ndef __mul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __mul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __mul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __mul__(\n self: ndarray[Any, StringDType | dtype[character]],\n other: _ArrayLikeInt,\n /\n) -> ndarray: ...\ndef __mul__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __mul__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__mul__", "sortText": "3.9999.__mul__"}, {"detail": "def __ne__(\n self: ndarray,\n other: Any,\n /\n) -> Any: ...", "kind": 2, "label": "__ne__", "sortText": "3.9999.__ne__"}, {"detail": "def __neg__[ArrayT: ndarray[tuple[Any, ...], dtype[number | object_ | timedelta64]]](self: ArrayT, /) -> ArrayT: ...", "kind": 2, "label": "__neg__", "sortText": "3.9999.__neg__"}, {"detail": "def __new__[Self@ndarray: ndarray](\n cls : type[Self@ndarray],\n shape : _ShapeLike,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = ...,\n buffer : Buffer | None = ...,\n offset : SupportsIndex = ...,\n strides: Sequence[SupportsIndex] | SupportsIndex | None = ...,\n order : _OrderKACF = ...\n) -> Self@ndarray: ...", "kind": 5, "label": "__new__", "sortText": "3.9999.__new__"}, {"detail": "\n@overload\ndef __or__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __or__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __or__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __or__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __or__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__or__", "sortText": "3.9999.__or__"}, {"detail": "def __pos__[ArrayT: ndarray[tuple[Any, ...], dtype[number | object_ | timedelta64]]](self: ArrayT, /) -> ArrayT: ...", "kind": 2, "label": "__pos__", "sortText": "3.9999.__pos__"}, {"detail": "\n@overload\ndef __pow__[ScalarT: number](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __pow__[ScalarT: number](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __pow__[ScalarT: number](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __pow__(\n self : ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n mod : None = None,\n /\n) -> Any: ...\ndef __pow__(\n self : ndarray,\n other: _ArrayLikeObject_co,\n mod : None = None,\n /\n) -> Any: ...", "kind": 2, "label": "__pow__", "sortText": "3.9999.__pow__"}, {"detail": "\n@overload\ndef __radd__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __radd__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __radd__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[datetime64]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[datetime64]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[bytes_]],\n other: _ArrayLikeBytes_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[bytes_]]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[str_]],\n other: _ArrayLikeStr_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[str_]]: ...\ndef __radd__(\n self: ndarray[Any, StringDType],\n other: _NestedSequence[_SupportsArray[StringDType]] | _NestedSequence[_SupportsArray[dtype[str_]]] | _NestedSequence[str] | _SupportsArray[StringDType] | _SupportsArray[dtype[str_]] | str,\n /\n) -> ndarray[tuple[Any, ...], StringDType]: ...\ndef __radd__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __radd__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__radd__", "sortText": "3.9999.__radd__"}, {"detail": "\n@overload\ndef __rand__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __rand__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rand__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rand__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rand__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rand__", "sortText": "3.9999.__rand__"}, {"detail": "\n@overload\ndef __rdivmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n lhs: numpy.bool | int,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[ScalarT]], ndarray[tuple[Any, ...], dtype[ScalarT]]]: ...\ndef __rdivmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n lhs: _ArrayLikeBool_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[ScalarT]], ndarray[tuple[Any, ...], dtype[ScalarT]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n lhs: _ArrayLikeBool_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]], ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]]: ...\ndef __rdivmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n lhs: _ArrayLike,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[ScalarT]], ndarray[tuple[Any, ...], dtype[ScalarT]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n lhs: _ArrayLikeFloat64_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n lhs: _ArrayLike,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[float64]], ndarray[tuple[Any, ...], dtype[float64]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n lhs: _ArrayLikeUInt_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[unsignedinteger]], ndarray[tuple[Any, ...], dtype[unsignedinteger]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n lhs: _ArrayLikeInt_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[signedinteger]], ndarray[tuple[Any, ...], dtype[signedinteger]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n lhs: _ArrayLikeFloat_co,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[floating]], ndarray[tuple[Any, ...], dtype[floating]]]: ...\ndef __rdivmod__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n lhs: _ArrayLike,\n /\n) -> tuple[ndarray[tuple[Any, ...], dtype[signedinteger[_64Bit]]], ndarray[tuple[Any, ...], dtype[timedelta64]]]: ...", "kind": 2, "label": "__rdivmod__", "sortText": "3.9999.__rdivmod__"}, {"detail": "\n@overload\ndef __rfloordiv__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rfloordiv__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __rfloordiv__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_64Bit]]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[floating | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rfloordiv__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rfloordiv__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rfloordiv__", "sortText": "3.9999.__rfloordiv__"}, {"detail": "\n@overload\ndef __rlshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __rlshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rlshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rlshift__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rlshift__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rlshift__", "sortText": "3.9999.__rlshift__"}, {"detail": "\n@overload\ndef __rmatmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __rmatmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[floating[_64Bit]]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[complexfloating[_64Bit, _64Bit]]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __rmatmul__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rmatmul__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rmatmul__", "sortText": "3.9999.__rmatmul__"}, {"detail": "\n@overload\ndef __rmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __rmod__[ScalarT: floating | integer](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rmod__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rmod__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rmod__", "sortText": "3.9999.__rmod__"}, {"detail": "\n@overload\ndef __rmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __rmul__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rmul__(\n self: ndarray[Any, StringDType | dtype[character]],\n other: _ArrayLikeInt,\n /\n) -> ndarray: ...\ndef __rmul__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rmul__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rmul__", "sortText": "3.9999.__rmul__"}, {"detail": "\n@overload\ndef __ror__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __ror__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __ror__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __ror__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __ror__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__ror__", "sortText": "3.9999.__ror__"}, {"detail": "\n@overload\ndef __rpow__[ScalarT: number](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rpow__[ScalarT: number](\n self : ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __rpow__[ScalarT: number](\n self : ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n mod : None = None,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __rpow__(\n self : ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n mod : None = None,\n /\n) -> Any: ...\ndef __rpow__(\n self : ndarray,\n other: _ArrayLikeObject_co,\n mod : None = None,\n /\n) -> Any: ...", "kind": 2, "label": "__rpow__", "sortText": "3.9999.__rpow__"}, {"detail": "\n@overload\ndef __rrshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __rrshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rrshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rrshift__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rrshift__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rrshift__", "sortText": "3.9999.__rrshift__"}, {"detail": "\n@overload\ndef __rshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger[_8Bit]]]: ...\ndef __rshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rshift__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rshift__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rshift__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rshift__", "sortText": "3.9999.__rshift__"}, {"detail": "\n@overload\ndef __rsub__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rsub__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> NoReturn: ...\ndef __rsub__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[datetime64]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rsub__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rsub__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rsub__", "sortText": "3.9999.__rsub__"}, {"detail": "\n@overload\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]] | ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _NestedSequence[_SupportsArray[dtype[numpy.bool | integer]]] | _NestedSequence[_SupportsArray[dtype[floating[_64Bit]]]] | _NestedSequence[int] | _SupportsArray[dtype[numpy.bool | integer]] | _SupportsArray[dtype[floating[_64Bit]]] | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[floating]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[complexfloating]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[inexact]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[inexact]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[floating | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __rtruediv__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rtruediv__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rtruediv__", "sortText": "3.9999.__rtruediv__"}, {"detail": "\n@overload\ndef __rxor__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __rxor__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __rxor__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __rxor__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __rxor__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__rxor__", "sortText": "3.9999.__rxor__"}, {"detail": "\n@overload\ndef __setitem__(\n self: ndarray[Any, StringDType | dtype[numpy.bool | flexible | object_]],\n key: _ToIndices,\n value: object,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray[tuple[Any, ...], dtype[integer]],\n key: _ToIndices,\n value: SupportsIndex | SupportsInt | _NestedSequence[_ConvertibleToInt] | _NestedSequence[_SupportsArray[dtype[numpy.bool | integer]]] | _NestedSequence[int] | _SupportsArray[dtype[numpy.bool | integer]] | bytes | int | str,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray[tuple[Any, ...], dtype[floating]],\n key: _ToIndices,\n value: SupportsFloat | SupportsIndex | _NestedSequence[SupportsFloat | SupportsIndex | bytes | str | None] | _NestedSequence[_SupportsArray[dtype[numpy.bool | floating | integer]]] | _NestedSequence[float] | _SupportsArray[dtype[numpy.bool | floating | integer]] | bytes | float | str | None,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray[tuple[Any, ...], dtype[complexfloating]],\n key: _ToIndices,\n value: SupportsComplex | SupportsFloat | SupportsIndex | _NestedSequence[SupportsComplex | SupportsFloat | SupportsIndex | bytes | str | None] | _NestedSequence[_SupportsArray[dtype[numpy.bool | number]]] | _NestedSequence[complex] | _SupportsArray[dtype[numpy.bool | number]] | bytes | complex | str | None,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n key: _ToIndices,\n value: _NestedSequence[_ConvertibleToTD64] | numpy.bool | bytes | character | int | number | str | timedelta | timedelta64 | None,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n key: _ToIndices,\n value: _NestedSequence[_ConvertibleToDT64] | numpy.bool | bytes | character | date | datetime64 | int | number | str | None,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray[tuple[Any, ...], dtype[void]],\n key: list[str] | str,\n value: object,\n /\n) -> None: ...\ndef __setitem__(\n self: ndarray,\n key: _ToIndices,\n value: ArrayLike,\n /\n) -> None: ...", "kind": 2, "label": "__setitem__", "sortText": "3.9999.__setitem__"}, {"detail": "def __setstate__[_DTypeT_co: dtype = dtype](\n self: ndarray,\n state: tuple[SupportsIndex, _ShapeLike, _DTypeT_co, numpy.bool, bytes | list[Any]],\n /\n) -> None: ...", "kind": 2, "label": "__setstate__", "sortText": "3.9999.__setstate__"}, {"detail": "\n@overload\ndef __sub__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: numpy.bool | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __sub__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[ScalarT]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> NoReturn: ...\ndef __sub__[ScalarT: number](\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[ScalarT]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | inexact | integer]],\n other: _ArrayLikeComplex_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer | timedelta64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeTD64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[datetime64]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[datetime64]],\n other: _ArrayLikeDT64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __sub__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __sub__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__sub__", "sortText": "3.9999.__sub__"}, {"detail": "\n@overload\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]] | ndarray[tuple[Any, ...], dtype[float64]],\n other: _ArrayLikeFloat64_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | floating[_32Bit] | floating[_64Bit] | integer]],\n other: _NestedSequence[_SupportsArray[dtype[numpy.bool | integer]]] | _NestedSequence[_SupportsArray[dtype[floating[_64Bit]]]] | _NestedSequence[int] | _SupportsArray[dtype[numpy.bool | integer]] | _SupportsArray[dtype[floating[_64Bit]]] | int,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[complex128]],\n other: _ArrayLikeComplex128_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating[_16Bit] | integer | number[_32Bit] | number[_64Bit]]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complex128]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[floating]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | floating | integer]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[floating]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[complexfloating]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | number]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[complexfloating]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[inexact]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[inexact]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[number]],\n other: _ArrayLikeNumber_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[number]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLike,\n /\n) -> ndarray[tuple[Any, ...], dtype[float64]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLikeBool_co,\n /\n) -> NoReturn: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[timedelta64]],\n other: _ArrayLikeFloat_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[timedelta64]]: ...\ndef __truediv__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __truediv__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__truediv__", "sortText": "3.9999.__truediv__"}, {"detail": "\n@overload\ndef __xor__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other: _ArrayLikeBool_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[numpy.bool]]: ...\ndef __xor__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | unsignedinteger]],\n other: _ArrayLikeUInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[unsignedinteger]]: ...\ndef __xor__(\n self: ndarray[tuple[Any, ...], dtype[numpy.bool | integer]],\n other: _ArrayLikeInt_co,\n /\n) -> ndarray[tuple[Any, ...], dtype[signedinteger]]: ...\ndef __xor__(\n self: ndarray[tuple[Any, ...], dtype[object_]],\n other: Any,\n /\n) -> Any: ...\ndef __xor__(\n self: ndarray,\n other: _ArrayLikeObject_co,\n /\n) -> Any: ...", "kind": 2, "label": "__xor__", "sortText": "3.9999.__xor__"}]}} -{"suite": "data_science", "label": "edit array then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 10, "character": 15, "iteration": 1, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) array: def array(\n object: Any,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n copy : _CopyMode | bool | None = True,\n order : _OrderKACF = 'K',\n subok : bool = False,\n ndmin : int = 0,\n ndmax : int = 0,\n like : _SupportsArrayFunc | None = None\n) -> ndarray: ...\n```\n\nGo to [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [_HasDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L37,7) | [_HasNumPyDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L43,7) | [list](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3464,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [_DTypeDict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L24,7) | [_CopyMode](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_globals.pyi#L7,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [_SupportsArrayFunc](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_array_like.py#L28,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7)"}}} -{"suite": "data_science", "label": "edit array then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 10, "character": 15, "iteration": 2, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) array: def array(\n object: Any,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n copy : _CopyMode | bool | None = True,\n order : _OrderKACF = 'K',\n subok : bool = False,\n ndmin : int = 0,\n ndmax : int = 0,\n like : _SupportsArrayFunc | None = None\n) -> ndarray: ...\n```\n\nGo to [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [_HasDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L37,7) | [_HasNumPyDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L43,7) | [list](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3464,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [_DTypeDict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L24,7) | [_CopyMode](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_globals.pyi#L7,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [_SupportsArrayFunc](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_array_like.py#L28,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7)"}}} -{"suite": "data_science", "label": "edit array then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 10, "character": 15, "iteration": 3, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) array: def array(\n object: Any,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n copy : _CopyMode | bool | None = True,\n order : _OrderKACF = 'K',\n subok : bool = False,\n ndmin : int = 0,\n ndmax : int = 0,\n like : _SupportsArrayFunc | None = None\n) -> ndarray: ...\n```\n\nGo to [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [_HasDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L37,7) | [_HasNumPyDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L43,7) | [list](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3464,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [_DTypeDict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L24,7) | [_CopyMode](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_globals.pyi#L7,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [_SupportsArrayFunc](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_array_like.py#L28,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7)"}}} -{"suite": "data_science", "label": "edit array then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 10, "character": 15, "iteration": 4, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) array: def array(\n object: Any,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n copy : _CopyMode | bool | None = True,\n order : _OrderKACF = 'K',\n subok : bool = False,\n ndmin : int = 0,\n ndmax : int = 0,\n like : _SupportsArrayFunc | None = None\n) -> ndarray: ...\n```\n\nGo to [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [_HasDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L37,7) | [_HasNumPyDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L43,7) | [list](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3464,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [_DTypeDict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L24,7) | [_CopyMode](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_globals.pyi#L7,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [_SupportsArrayFunc](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_array_like.py#L28,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7)"}}} -{"suite": "data_science", "label": "edit array then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 10, "character": 15, "iteration": 5, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) array: def array(\n object: Any,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n copy : _CopyMode | bool | None = True,\n order : _OrderKACF = 'K',\n subok : bool = False,\n ndmin : int = 0,\n ndmax : int = 0,\n like : _SupportsArrayFunc | None = None\n) -> ndarray: ...\n```\n\nGo to [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [_HasDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L37,7) | [_HasNumPyDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L43,7) | [list](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3464,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [_DTypeDict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L24,7) | [_CopyMode](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_globals.pyi#L7,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [_SupportsArrayFunc](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_array_like.py#L28,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7)"}}} +{"suite": "data_science", "label": "edit array then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 10, "character": 15, "iteration": 1, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) array: def array(\n object: Any,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n copy : _CopyMode | bool | None = True,\n order : _OrderKACF = 'K',\n subok : bool = False,\n ndmin : int = 0,\n ndmax : int = 0,\n like : _SupportsArrayFunc | None = None\n) -> ndarray: ...\n```\n\nGo to [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [_HasDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L37,7) | [_HasNumPyDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L43,7) | [list](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3464,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [_DTypeDict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L24,7) | [_CopyMode](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_globals.pyi#L7,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [_SupportsArrayFunc](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_array_like.py#L28,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7)"}}} +{"suite": "data_science", "label": "edit array then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 10, "character": 15, "iteration": 2, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) array: def array(\n object: Any,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n copy : _CopyMode | bool | None = True,\n order : _OrderKACF = 'K',\n subok : bool = False,\n ndmin : int = 0,\n ndmax : int = 0,\n like : _SupportsArrayFunc | None = None\n) -> ndarray: ...\n```\n\nGo to [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [_HasDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L37,7) | [_HasNumPyDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L43,7) | [list](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3464,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [_DTypeDict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L24,7) | [_CopyMode](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_globals.pyi#L7,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [_SupportsArrayFunc](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_array_like.py#L28,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7)"}}} +{"suite": "data_science", "label": "edit array then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 10, "character": 15, "iteration": 3, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) array: def array(\n object: Any,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n copy : _CopyMode | bool | None = True,\n order : _OrderKACF = 'K',\n subok : bool = False,\n ndmin : int = 0,\n ndmax : int = 0,\n like : _SupportsArrayFunc | None = None\n) -> ndarray: ...\n```\n\nGo to [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [_HasDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L37,7) | [_HasNumPyDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L43,7) | [list](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3464,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [_DTypeDict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L24,7) | [_CopyMode](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_globals.pyi#L7,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [_SupportsArrayFunc](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_array_like.py#L28,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7)"}}} +{"suite": "data_science", "label": "edit array then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 10, "character": 15, "iteration": 4, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) array: def array(\n object: Any,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n copy : _CopyMode | bool | None = True,\n order : _OrderKACF = 'K',\n subok : bool = False,\n ndmin : int = 0,\n ndmax : int = 0,\n like : _SupportsArrayFunc | None = None\n) -> ndarray: ...\n```\n\nGo to [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [_HasDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L37,7) | [_HasNumPyDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L43,7) | [list](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3464,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [_DTypeDict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L24,7) | [_CopyMode](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_globals.pyi#L7,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [_SupportsArrayFunc](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_array_like.py#L28,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7)"}}} +{"suite": "data_science", "label": "edit array then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/src/analysis.py", "line": 10, "character": 15, "iteration": 5, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) array: def array(\n object: Any,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n *,\n copy : _CopyMode | bool | None = True,\n order : _OrderKACF = 'K',\n subok : bool = False,\n ndmin : int = 0,\n ndmax : int = 0,\n like : _SupportsArrayFunc | None = None\n) -> ndarray: ...\n```\n\nGo to [dtype](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L1086,7) | [_HasDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L37,7) | [_HasNumPyDType](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L43,7) | [list](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3464,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [_DTypeDict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_dtype_like.py#L24,7) | [_CopyMode](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_globals.pyi#L7,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [_SupportsArrayFunc](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/_typing/_array_like.py#L28,7) | [ndarray](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L2078,7)"}}} {"suite": "django", "label": "queryset completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 20, "character": 20, "iteration": 1, "result": {"isIncomplete": true, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"detail": "QuerySet[Article, Article]", "kind": 6, "label": "filtered", "sortText": "0.9999.filtered"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}]}} {"suite": "django", "label": "queryset completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 20, "character": 20, "iteration": 2, "result": {"isIncomplete": true, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"detail": "QuerySet[Article, Article]", "kind": 6, "label": "filtered", "sortText": "0.9999.filtered"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}]}} {"suite": "django", "label": "queryset completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 20, "character": 20, "iteration": 3, "result": {"isIncomplete": true, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"detail": "QuerySet[Article, Article]", "kind": 6, "label": "filtered", "sortText": "0.9999.filtered"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}]}} {"suite": "django", "label": "queryset completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 20, "character": 20, "iteration": 4, "result": {"isIncomplete": true, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"detail": "QuerySet[Article, Article]", "kind": 6, "label": "filtered", "sortText": "0.9999.filtered"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}]}} {"suite": "django", "label": "queryset completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 20, "character": 20, "iteration": 5, "result": {"isIncomplete": true, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"detail": "QuerySet[Article, Article]", "kind": 6, "label": "filtered", "sortText": "0.9999.filtered"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}]}} -{"suite": "django", "label": "queryset filter hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 18, "character": 23, "iteration": 1, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(function) load_published_articles: def load_published_articles() -> list[Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [list](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3464,7)"}}} -{"suite": "django", "label": "queryset filter hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 18, "character": 23, "iteration": 2, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(function) load_published_articles: def load_published_articles() -> list[Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [list](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3464,7)"}}} -{"suite": "django", "label": "queryset filter hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 18, "character": 23, "iteration": 3, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(function) load_published_articles: def load_published_articles() -> list[Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [list](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3464,7)"}}} -{"suite": "django", "label": "queryset filter hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 18, "character": 23, "iteration": 4, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(function) load_published_articles: def load_published_articles() -> list[Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [list](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3464,7)"}}} -{"suite": "django", "label": "queryset filter hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 18, "character": 23, "iteration": 5, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(function) load_published_articles: def load_published_articles() -> list[Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [list](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3464,7)"}}} +{"suite": "django", "label": "queryset filter hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 18, "character": 23, "iteration": 1, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(function) load_published_articles: def load_published_articles() -> list[Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [list](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3464,7)"}}} +{"suite": "django", "label": "queryset filter hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 18, "character": 23, "iteration": 2, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(function) load_published_articles: def load_published_articles() -> list[Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [list](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3464,7)"}}} +{"suite": "django", "label": "queryset filter hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 18, "character": 23, "iteration": 3, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(function) load_published_articles: def load_published_articles() -> list[Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [list](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3464,7)"}}} +{"suite": "django", "label": "queryset filter hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 18, "character": 23, "iteration": 4, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(function) load_published_articles: def load_published_articles() -> list[Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [list](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3464,7)"}}} +{"suite": "django", "label": "queryset filter hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 18, "character": 23, "iteration": 5, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(function) load_published_articles: def load_published_articles() -> list[Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [list](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3464,7)"}}} {"suite": "django", "label": "model definition", "method": "textDocument/definition", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 19, "character": 14, "iteration": 1, "result": {"range": {"end": {"character": 13, "line": 7}, "start": {"character": 6, "line": 7}}, "uri": "file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py"}} {"suite": "django", "label": "model definition", "method": "textDocument/definition", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 19, "character": 14, "iteration": 2, "result": {"range": {"end": {"character": 13, "line": 7}, "start": {"character": 6, "line": 7}}, "uri": "file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py"}} {"suite": "django", "label": "model definition", "method": "textDocument/definition", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 19, "character": 14, "iteration": 3, "result": {"range": {"end": {"character": 13, "line": 7}, "start": {"character": 6, "line": 7}}, "uri": "file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py"}} @@ -43,21 +43,21 @@ {"suite": "django", "label": "edit queryset then complete (edit+completion)", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 20, "character": 48, "iteration": 3, "result": {"isIncomplete": false, "items": [{"detail": "def aaggregate(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> Coroutine[Unknown, Unknown, dict[str, Any]]: ...", "kind": 2, "label": "aaggregate", "sortText": "0.9999.aaggregate"}, {"detail": "def abulk_create(\n self : QuerySet[Article, Article],\n objs : Iterable[Article],\n batch_size : int | None = None,\n ignore_conflicts: bool = False,\n update_conflicts: bool = False,\n update_fields : Collection[str] | None = None,\n unique_fields : Collection[str] | None = None\n) -> Coroutine[Unknown, Unknown, list[Article]]: ...", "kind": 2, "label": "abulk_create", "sortText": "0.9999.abulk_create"}, {"detail": "def abulk_update(\n self : QuerySet[Article, Article],\n objs : Iterable[Article],\n fields : Iterable[str],\n batch_size: int | None = None\n) -> Coroutine[Unknown, Unknown, int]: ...", "kind": 2, "label": "abulk_update", "sortText": "0.9999.abulk_update"}, {"detail": "def acontains(\n self: QuerySet[Article, Article],\n obj: Model\n) -> Coroutine[Unknown, Unknown, bool]: ...", "kind": 2, "label": "acontains", "sortText": "0.9999.acontains"}, {"detail": "def acount(self: QuerySet[Article, Article]) -> Coroutine[Unknown, Unknown, int]: ...", "kind": 2, "label": "acount", "sortText": "0.9999.acount"}, {"detail": "def acreate(\n self: QuerySet[Article, Article],\n **kwargs: Any\n) -> Coroutine[Unknown, Unknown, Article]: ...", "kind": 2, "label": "acreate", "sortText": "0.9999.acreate"}, {"detail": "def adelete(self: QuerySet[Article, Article]) -> Coroutine[Unknown, Unknown, tuple[int, dict[str, int]]]: ...", "kind": 2, "label": "adelete", "sortText": "0.9999.adelete"}, {"detail": "def aearliest(\n self: QuerySet[Article, Article],\n *fields: OrderBy | str\n) -> Coroutine[Unknown, Unknown, Article]: ...", "kind": 2, "label": "aearliest", "sortText": "0.9999.aearliest"}, {"detail": "def aexists(self: QuerySet[Article, Article]) -> Coroutine[Unknown, Unknown, bool]: ...", "kind": 2, "label": "aexists", "sortText": "0.9999.aexists"}, {"detail": "def aexplain(\n self : QuerySet[Article, Article],\n *,\n format : str | None = None,\n **options: Any\n) -> Coroutine[Unknown, Unknown, str]: ...", "kind": 2, "label": "aexplain", "sortText": "0.9999.aexplain"}, {"detail": "def afirst(self: QuerySet[Article, Article]) -> Coroutine[Unknown, Unknown, Article | None]: ...", "kind": 2, "label": "afirst", "sortText": "0.9999.afirst"}, {"detail": "def aget(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> Coroutine[Unknown, Unknown, Article]: ...", "kind": 2, "label": "aget", "sortText": "0.9999.aget"}, {"detail": "def aget_or_create(\n self : QuerySet[Article, Article],\n defaults: Mapping[str, Any] | None = None,\n **kwargs: Any\n) -> Coroutine[Unknown, Unknown, tuple[Article, bool]]: ...", "kind": 2, "label": "aget_or_create", "sortText": "0.9999.aget_or_create"}, {"detail": "def aggregate(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> dict[str, Any]: ...", "kind": 2, "label": "aggregate", "sortText": "0.9999.aggregate"}, {"detail": "def ain_bulk(\n self : QuerySet[Article, Article],\n id_list : Iterable[Any] | None = None,\n *,\n field_name: str = 'pk'\n) -> Coroutine[Unknown, Unknown, dict[Any, Article]]: ...", "kind": 2, "label": "ain_bulk", "sortText": "0.9999.ain_bulk"}, {"detail": "def aiterator(\n self : QuerySet[Article, Article],\n chunk_size: int = 2000\n) -> AsyncIterator[Article]: ...", "kind": 2, "label": "aiterator", "sortText": "0.9999.aiterator"}, {"detail": "def alast(self: QuerySet[Article, Article]) -> Coroutine[Unknown, Unknown, Article | None]: ...", "kind": 2, "label": "alast", "sortText": "0.9999.alast"}, {"detail": "def alatest(\n self: QuerySet[Article, Article],\n *fields: OrderBy | str\n) -> Coroutine[Unknown, Unknown, Article]: ...", "kind": 2, "label": "alatest", "sortText": "0.9999.alatest"}, {"detail": "def alias(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "alias", "sortText": "0.9999.alias"}, {"detail": "def all(self: QuerySet[Article, Article]) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "all", "sortText": "0.9999.all"}, {"detail": "def annotate(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "annotate", "sortText": "0.9999.annotate"}, {"detail": "def as_manager(cls: type[QuerySet[Article, Article]]) -> Manager[Article]: ...", "kind": 2, "label": "as_manager", "sortText": "0.9999.as_manager"}, {"detail": "def aupdate(\n self: QuerySet[Article, Article],\n **kwargs: Any\n) -> Coroutine[Unknown, Unknown, int]: ...", "kind": 2, "label": "aupdate", "sortText": "0.9999.aupdate"}, {"detail": "def aupdate_or_create(\n self : QuerySet[Article, Article],\n defaults : Mapping[str, Any] | None = None,\n create_defaults: Mapping[str, Any] | None = None,\n **kwargs : Any\n) -> Coroutine[Unknown, Unknown, tuple[Article, bool]]: ...", "kind": 2, "label": "aupdate_or_create", "sortText": "0.9999.aupdate_or_create"}, {"detail": "def bulk_create(\n self : QuerySet[Article, Article],\n objs : Iterable[Article],\n batch_size : int | None = None,\n ignore_conflicts: bool = False,\n update_conflicts: bool = False,\n update_fields : Collection[str] | None = None,\n unique_fields : Collection[str] | None = None\n) -> list[Article]: ...", "kind": 2, "label": "bulk_create", "sortText": "0.9999.bulk_create"}, {"detail": "def bulk_update(\n self : QuerySet[Article, Article],\n objs : Iterable[Article],\n fields : Iterable[str],\n batch_size: int | None = None\n) -> int: ...", "kind": 2, "label": "bulk_update", "sortText": "0.9999.bulk_update"}, {"detail": "def complex_filter(\n self: QuerySet[Article, Article],\n filter_obj: Any\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "complex_filter", "sortText": "0.9999.complex_filter"}, {"detail": "def contains(\n self: QuerySet[Article, Article],\n obj: Model\n) -> bool: ...", "kind": 2, "label": "contains", "sortText": "0.9999.contains"}, {"detail": "def count(self: QuerySet[Article, Article]) -> int: ...", "kind": 2, "label": "count", "sortText": "0.9999.count"}, {"detail": "def create(\n self: QuerySet[Article, Article],\n **kwargs: Any\n) -> Article: ...", "kind": 2, "label": "create", "sortText": "0.9999.create"}, {"detail": "def dates(\n self : QuerySet[Article, Article],\n field_name: str,\n kind : Literal['day', 'month', 'week', 'year'],\n order : Literal['ASC', 'DESC'] = 'ASC'\n) -> QuerySet[Article, date]: ...", "kind": 2, "label": "dates", "sortText": "0.9999.dates"}, {"detail": "def datetimes(\n self : QuerySet[Article, Article],\n field_name: str,\n kind : Literal['day', 'hour', 'minute', 'month', 'second', 'week', 'year'],\n order : Literal['ASC', 'DESC'] = 'ASC',\n tzinfo : tzinfo | None = None\n) -> QuerySet[Article, datetime]: ...", "kind": 2, "label": "datetimes", "sortText": "0.9999.datetimes"}, {"detail": "str", "kind": 5, "label": "db", "sortText": "0.9999.db"}, {"detail": "\n@overload\ndef defer(\n self: QuerySet[Article, Article],\n clear: None,\n /\n) -> QuerySet[Article, Article]: ...\ndef defer(\n self: QuerySet[Article, Article],\n *fields: str\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "defer", "sortText": "0.9999.defer"}, {"detail": "def delete(self: QuerySet[Article, Article]) -> tuple[int, dict[str, int]]: ...", "kind": 2, "label": "delete", "sortText": "0.9999.delete"}, {"detail": "def difference(\n self: QuerySet[Article, Article],\n *other_qs: QuerySet[Model, Any]\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "difference", "sortText": "0.9999.difference"}, {"detail": "def distinct(\n self: QuerySet[Article, Article],\n *field_names: str\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "distinct", "sortText": "0.9999.distinct"}, {"detail": "def earliest(\n self: QuerySet[Article, Article],\n *fields: OrderBy | str\n) -> Article: ...", "kind": 2, "label": "earliest", "sortText": "0.9999.earliest"}, {"detail": "def exclude(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "exclude", "sortText": "0.9999.exclude"}, {"detail": "def exists(self: QuerySet[Article, Article]) -> bool: ...", "kind": 2, "label": "exists", "sortText": "0.9999.exists"}, {"detail": "def explain(\n self : QuerySet[Article, Article],\n *,\n format : str | None = None,\n **options: Any\n) -> str: ...", "kind": 2, "label": "explain", "sortText": "0.9999.explain"}, {"detail": "def extra(\n self : QuerySet[Article, Article],\n select : dict[str, Any] | None = None,\n where : Sequence[str] | None = None,\n params : Sequence[Any] | None = None,\n tables : Sequence[str] | None = None,\n order_by : Sequence[_OrderByFieldName] | None = None,\n select_params: Sequence[Any] | None = None\n) -> QuerySet[Any, Any]: ...", "kind": 2, "label": "extra", "sortText": "0.9999.extra"}, {"detail": "def filter(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "filter", "sortText": "0.9999.filter"}, {"detail": "def first(self: QuerySet[Article, Article]) -> Article | None: ...", "kind": 2, "label": "first", "sortText": "0.9999.first"}, {"detail": "def get(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> Article: ...", "kind": 2, "label": "get", "sortText": "0.9999.get"}, {"detail": "def get_or_create(\n self : QuerySet[Article, Article],\n defaults: Mapping[str, Any] | None = None,\n **kwargs: Any\n) -> tuple[Article, bool]: ...", "kind": 2, "label": "get_or_create", "sortText": "0.9999.get_or_create"}, {"detail": "def in_bulk(\n self : QuerySet[Article, Article],\n id_list : Iterable[Any] | None = None,\n *,\n field_name: str = 'pk'\n) -> dict[Any, Article]: ...", "kind": 2, "label": "in_bulk", "sortText": "0.9999.in_bulk"}, {"detail": "def intersection(\n self: QuerySet[Article, Article],\n *other_qs: QuerySet[Model, Any]\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "intersection", "sortText": "0.9999.intersection"}, {"detail": "def iterator(\n self : QuerySet[Article, Article],\n chunk_size: int | None = None\n) -> Iterator[Article]: ...", "kind": 2, "label": "iterator", "sortText": "0.9999.iterator"}, {"detail": "def last(self: QuerySet[Article, Article]) -> Article | None: ...", "kind": 2, "label": "last", "sortText": "0.9999.last"}, {"detail": "def latest(\n self: QuerySet[Article, Article],\n *fields: OrderBy | str\n) -> Article: ...", "kind": 2, "label": "latest", "sortText": "0.9999.latest"}, {"detail": "type[Article]", "kind": 5, "label": "model", "sortText": "0.9999.model"}, {"detail": "def none(self: QuerySet[Article, Article]) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "none", "sortText": "0.9999.none"}, {"detail": "def only(\n self: QuerySet[Article, Article],\n *fields: str\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "only", "sortText": "0.9999.only"}, {"detail": "def order_by(\n self: QuerySet[Article, Article],\n *field_names: _OrderByFieldName\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "order_by", "sortText": "0.9999.order_by"}, {"detail": "bool", "kind": 5, "label": "ordered", "sortText": "0.9999.ordered"}, {"detail": "\n@overload\ndef prefetch_related(\n self: QuerySet[Article, Article],\n clear: None,\n /\n) -> QuerySet[Article, Article]: ...\ndef prefetch_related[_LookupT: str, _PrefetchedQuerySetT: QuerySet[Model, Model] = QuerySet[Model, Model], _ToAttrT: str = str](\n self: QuerySet[Article, Article],\n *lookups: Prefetch[_LookupT, _PrefetchedQuerySetT, _ToAttrT] | str\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "prefetch_related", "sortText": "0.9999.prefetch_related"}, {"detail": "Query", "kind": 5, "label": "query", "sortText": "0.9999.query"}, {"detail": "def raw(\n self : QuerySet[Article, Article],\n raw_query : _ExecuteQuery,\n params : Any = ...,\n translations: dict[str, str] | None = None,\n using : str | None = None\n) -> RawQuerySet[Article]: ...", "kind": 2, "label": "raw", "sortText": "0.9999.raw"}, {"detail": "def resolve_expression(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> Any: ...", "kind": 2, "label": "resolve_expression", "sortText": "0.9999.resolve_expression"}, {"detail": "def reverse(self: QuerySet[Article, Article]) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "reverse", "sortText": "0.9999.reverse"}, {"detail": "def select_for_update(\n self : QuerySet[Article, Article],\n nowait : bool = False,\n skip_locked: bool = False,\n of : Sequence[str] = ...,\n no_key : bool = False\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "select_for_update", "sortText": "0.9999.select_for_update"}, {"detail": "\n@overload\ndef select_related(\n self: QuerySet[Article, Article],\n clear: None,\n /\n) -> QuerySet[Article, Article]: ...\ndef select_related(\n self: QuerySet[Article, Article],\n *fields: str\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "select_related", "sortText": "0.9999.select_related"}, {"detail": "def union(\n self : QuerySet[Article, Article],\n *other_qs: QuerySet[Model, Any],\n *,\n all : bool = False\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "union", "sortText": "0.9999.union"}, {"detail": "def update(\n self: QuerySet[Article, Article],\n **kwargs: Any\n) -> int: ...", "kind": 2, "label": "update", "sortText": "0.9999.update"}, {"detail": "def update_or_create(\n self : QuerySet[Article, Article],\n defaults : Mapping[str, Any] | None = None,\n create_defaults: Mapping[str, Any] | None = None,\n **kwargs : Any\n) -> tuple[Article, bool]: ...", "kind": 2, "label": "update_or_create", "sortText": "0.9999.update_or_create"}, {"detail": "def using(\n self: QuerySet[Article, Article],\n alias: str | None\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "using", "sortText": "0.9999.using"}, {"detail": "def values(\n self: QuerySet[Article, Article],\n *fields: Combinable | str,\n **expressions: Any\n) -> QuerySet[Article, dict[str, Any]]: ...", "kind": 2, "label": "values", "sortText": "0.9999.values"}, {"detail": "def values_list(\n self : QuerySet[Article, Article],\n *fields: Combinable | str,\n *,\n flat : bool = False,\n named : bool = False\n) -> QuerySet[Article, Any]: ...", "kind": 2, "label": "values_list", "sortText": "0.9999.values_list"}, {"detail": "def _fetch_all(self: QuerySet[Article, Article]) -> None: ...", "kind": 2, "label": "_fetch_all", "sortText": "2.9999._fetch_all"}, {"detail": "type[BaseIterable[Unknown]]", "kind": 5, "label": "_iterable_class", "sortText": "2.9999._iterable_class"}, {"detail": "def _raw_delete(\n self: QuerySet[Article, Article],\n using: str | None\n) -> int: ...", "kind": 2, "label": "_raw_delete", "sortText": "2.9999._raw_delete"}, {"detail": "list[Article] | None", "kind": 5, "label": "_result_cache", "sortText": "2.9999._result_cache"}, {"detail": "def __aiter__(self: QuerySet[Article, Article]) -> AsyncIterator[Article]: ...", "kind": 2, "label": "__aiter__", "sortText": "3.9999.__aiter__"}, {"detail": "def __and__(\n self: QuerySet[Article, Article],\n other: QuerySet[Article, Article]\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "__and__", "sortText": "3.9999.__and__"}, {"detail": "def __bool__(self: QuerySet[Article, Article]) -> bool: ...", "kind": 2, "label": "__bool__", "sortText": "3.9999.__bool__"}, {"detail": "def __class_getitem__(\n cls: type[QuerySet[Article, Article]],\n item: type[Article]\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "__class_getitem__", "sortText": "3.9999.__class_getitem__"}, {"detail": "\n@overload\ndef __getitem__(\n self: QuerySet[Article, Article],\n i: int\n) -> Article: ...\ndef __getitem__(\n self: QuerySet[Article, Article],\n s: slice[Any, Any, Any]\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "__getitem__", "sortText": "3.9999.__getitem__"}, {"detail": "def __getstate__(self: QuerySet[Article, Article]) -> dict[str, Any]: ...", "kind": 2, "label": "__getstate__", "sortText": "3.9999.__getstate__"}, {"detail": "def __init__(\n self : QuerySet[Article, Article],\n model: type[Model] | None = None,\n query: Query | None = None,\n using: str | None = None,\n hints: dict[str, Model] | None = None\n) -> None: ...", "kind": 2, "label": "__init__", "sortText": "3.9999.__init__"}, {"detail": "def __iter__(self: QuerySet[Article, Article]) -> Iterator[Article]: ...", "kind": 2, "label": "__iter__", "sortText": "3.9999.__iter__"}, {"detail": "def __len__(self: QuerySet[Article, Article]) -> int: ...", "kind": 2, "label": "__len__", "sortText": "3.9999.__len__"}, {"detail": "def __or__(\n self: QuerySet[Article, Article],\n other: QuerySet[Article, Article]\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "__or__", "sortText": "3.9999.__or__"}]}} {"suite": "django", "label": "edit queryset then complete (edit+completion)", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 20, "character": 48, "iteration": 4, "result": {"isIncomplete": false, "items": [{"detail": "def aaggregate(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> Coroutine[Unknown, Unknown, dict[str, Any]]: ...", "kind": 2, "label": "aaggregate", "sortText": "0.9999.aaggregate"}, {"detail": "def abulk_create(\n self : QuerySet[Article, Article],\n objs : Iterable[Article],\n batch_size : int | None = None,\n ignore_conflicts: bool = False,\n update_conflicts: bool = False,\n update_fields : Collection[str] | None = None,\n unique_fields : Collection[str] | None = None\n) -> Coroutine[Unknown, Unknown, list[Article]]: ...", "kind": 2, "label": "abulk_create", "sortText": "0.9999.abulk_create"}, {"detail": "def abulk_update(\n self : QuerySet[Article, Article],\n objs : Iterable[Article],\n fields : Iterable[str],\n batch_size: int | None = None\n) -> Coroutine[Unknown, Unknown, int]: ...", "kind": 2, "label": "abulk_update", "sortText": "0.9999.abulk_update"}, {"detail": "def acontains(\n self: QuerySet[Article, Article],\n obj: Model\n) -> Coroutine[Unknown, Unknown, bool]: ...", "kind": 2, "label": "acontains", "sortText": "0.9999.acontains"}, {"detail": "def acount(self: QuerySet[Article, Article]) -> Coroutine[Unknown, Unknown, int]: ...", "kind": 2, "label": "acount", "sortText": "0.9999.acount"}, {"detail": "def acreate(\n self: QuerySet[Article, Article],\n **kwargs: Any\n) -> Coroutine[Unknown, Unknown, Article]: ...", "kind": 2, "label": "acreate", "sortText": "0.9999.acreate"}, {"detail": "def adelete(self: QuerySet[Article, Article]) -> Coroutine[Unknown, Unknown, tuple[int, dict[str, int]]]: ...", "kind": 2, "label": "adelete", "sortText": "0.9999.adelete"}, {"detail": "def aearliest(\n self: QuerySet[Article, Article],\n *fields: OrderBy | str\n) -> Coroutine[Unknown, Unknown, Article]: ...", "kind": 2, "label": "aearliest", "sortText": "0.9999.aearliest"}, {"detail": "def aexists(self: QuerySet[Article, Article]) -> Coroutine[Unknown, Unknown, bool]: ...", "kind": 2, "label": "aexists", "sortText": "0.9999.aexists"}, {"detail": "def aexplain(\n self : QuerySet[Article, Article],\n *,\n format : str | None = None,\n **options: Any\n) -> Coroutine[Unknown, Unknown, str]: ...", "kind": 2, "label": "aexplain", "sortText": "0.9999.aexplain"}, {"detail": "def afirst(self: QuerySet[Article, Article]) -> Coroutine[Unknown, Unknown, Article | None]: ...", "kind": 2, "label": "afirst", "sortText": "0.9999.afirst"}, {"detail": "def aget(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> Coroutine[Unknown, Unknown, Article]: ...", "kind": 2, "label": "aget", "sortText": "0.9999.aget"}, {"detail": "def aget_or_create(\n self : QuerySet[Article, Article],\n defaults: Mapping[str, Any] | None = None,\n **kwargs: Any\n) -> Coroutine[Unknown, Unknown, tuple[Article, bool]]: ...", "kind": 2, "label": "aget_or_create", "sortText": "0.9999.aget_or_create"}, {"detail": "def aggregate(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> dict[str, Any]: ...", "kind": 2, "label": "aggregate", "sortText": "0.9999.aggregate"}, {"detail": "def ain_bulk(\n self : QuerySet[Article, Article],\n id_list : Iterable[Any] | None = None,\n *,\n field_name: str = 'pk'\n) -> Coroutine[Unknown, Unknown, dict[Any, Article]]: ...", "kind": 2, "label": "ain_bulk", "sortText": "0.9999.ain_bulk"}, {"detail": "def aiterator(\n self : QuerySet[Article, Article],\n chunk_size: int = 2000\n) -> AsyncIterator[Article]: ...", "kind": 2, "label": "aiterator", "sortText": "0.9999.aiterator"}, {"detail": "def alast(self: QuerySet[Article, Article]) -> Coroutine[Unknown, Unknown, Article | None]: ...", "kind": 2, "label": "alast", "sortText": "0.9999.alast"}, {"detail": "def alatest(\n self: QuerySet[Article, Article],\n *fields: OrderBy | str\n) -> Coroutine[Unknown, Unknown, Article]: ...", "kind": 2, "label": "alatest", "sortText": "0.9999.alatest"}, {"detail": "def alias(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "alias", "sortText": "0.9999.alias"}, {"detail": "def all(self: QuerySet[Article, Article]) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "all", "sortText": "0.9999.all"}, {"detail": "def annotate(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "annotate", "sortText": "0.9999.annotate"}, {"detail": "def as_manager(cls: type[QuerySet[Article, Article]]) -> Manager[Article]: ...", "kind": 2, "label": "as_manager", "sortText": "0.9999.as_manager"}, {"detail": "def aupdate(\n self: QuerySet[Article, Article],\n **kwargs: Any\n) -> Coroutine[Unknown, Unknown, int]: ...", "kind": 2, "label": "aupdate", "sortText": "0.9999.aupdate"}, {"detail": "def aupdate_or_create(\n self : QuerySet[Article, Article],\n defaults : Mapping[str, Any] | None = None,\n create_defaults: Mapping[str, Any] | None = None,\n **kwargs : Any\n) -> Coroutine[Unknown, Unknown, tuple[Article, bool]]: ...", "kind": 2, "label": "aupdate_or_create", "sortText": "0.9999.aupdate_or_create"}, {"detail": "def bulk_create(\n self : QuerySet[Article, Article],\n objs : Iterable[Article],\n batch_size : int | None = None,\n ignore_conflicts: bool = False,\n update_conflicts: bool = False,\n update_fields : Collection[str] | None = None,\n unique_fields : Collection[str] | None = None\n) -> list[Article]: ...", "kind": 2, "label": "bulk_create", "sortText": "0.9999.bulk_create"}, {"detail": "def bulk_update(\n self : QuerySet[Article, Article],\n objs : Iterable[Article],\n fields : Iterable[str],\n batch_size: int | None = None\n) -> int: ...", "kind": 2, "label": "bulk_update", "sortText": "0.9999.bulk_update"}, {"detail": "def complex_filter(\n self: QuerySet[Article, Article],\n filter_obj: Any\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "complex_filter", "sortText": "0.9999.complex_filter"}, {"detail": "def contains(\n self: QuerySet[Article, Article],\n obj: Model\n) -> bool: ...", "kind": 2, "label": "contains", "sortText": "0.9999.contains"}, {"detail": "def count(self: QuerySet[Article, Article]) -> int: ...", "kind": 2, "label": "count", "sortText": "0.9999.count"}, {"detail": "def create(\n self: QuerySet[Article, Article],\n **kwargs: Any\n) -> Article: ...", "kind": 2, "label": "create", "sortText": "0.9999.create"}, {"detail": "def dates(\n self : QuerySet[Article, Article],\n field_name: str,\n kind : Literal['day', 'month', 'week', 'year'],\n order : Literal['ASC', 'DESC'] = 'ASC'\n) -> QuerySet[Article, date]: ...", "kind": 2, "label": "dates", "sortText": "0.9999.dates"}, {"detail": "def datetimes(\n self : QuerySet[Article, Article],\n field_name: str,\n kind : Literal['day', 'hour', 'minute', 'month', 'second', 'week', 'year'],\n order : Literal['ASC', 'DESC'] = 'ASC',\n tzinfo : tzinfo | None = None\n) -> QuerySet[Article, datetime]: ...", "kind": 2, "label": "datetimes", "sortText": "0.9999.datetimes"}, {"detail": "str", "kind": 5, "label": "db", "sortText": "0.9999.db"}, {"detail": "\n@overload\ndef defer(\n self: QuerySet[Article, Article],\n clear: None,\n /\n) -> QuerySet[Article, Article]: ...\ndef defer(\n self: QuerySet[Article, Article],\n *fields: str\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "defer", "sortText": "0.9999.defer"}, {"detail": "def delete(self: QuerySet[Article, Article]) -> tuple[int, dict[str, int]]: ...", "kind": 2, "label": "delete", "sortText": "0.9999.delete"}, {"detail": "def difference(\n self: QuerySet[Article, Article],\n *other_qs: QuerySet[Model, Any]\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "difference", "sortText": "0.9999.difference"}, {"detail": "def distinct(\n self: QuerySet[Article, Article],\n *field_names: str\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "distinct", "sortText": "0.9999.distinct"}, {"detail": "def earliest(\n self: QuerySet[Article, Article],\n *fields: OrderBy | str\n) -> Article: ...", "kind": 2, "label": "earliest", "sortText": "0.9999.earliest"}, {"detail": "def exclude(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "exclude", "sortText": "0.9999.exclude"}, {"detail": "def exists(self: QuerySet[Article, Article]) -> bool: ...", "kind": 2, "label": "exists", "sortText": "0.9999.exists"}, {"detail": "def explain(\n self : QuerySet[Article, Article],\n *,\n format : str | None = None,\n **options: Any\n) -> str: ...", "kind": 2, "label": "explain", "sortText": "0.9999.explain"}, {"detail": "def extra(\n self : QuerySet[Article, Article],\n select : dict[str, Any] | None = None,\n where : Sequence[str] | None = None,\n params : Sequence[Any] | None = None,\n tables : Sequence[str] | None = None,\n order_by : Sequence[_OrderByFieldName] | None = None,\n select_params: Sequence[Any] | None = None\n) -> QuerySet[Any, Any]: ...", "kind": 2, "label": "extra", "sortText": "0.9999.extra"}, {"detail": "def filter(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "filter", "sortText": "0.9999.filter"}, {"detail": "def first(self: QuerySet[Article, Article]) -> Article | None: ...", "kind": 2, "label": "first", "sortText": "0.9999.first"}, {"detail": "def get(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> Article: ...", "kind": 2, "label": "get", "sortText": "0.9999.get"}, {"detail": "def get_or_create(\n self : QuerySet[Article, Article],\n defaults: Mapping[str, Any] | None = None,\n **kwargs: Any\n) -> tuple[Article, bool]: ...", "kind": 2, "label": "get_or_create", "sortText": "0.9999.get_or_create"}, {"detail": "def in_bulk(\n self : QuerySet[Article, Article],\n id_list : Iterable[Any] | None = None,\n *,\n field_name: str = 'pk'\n) -> dict[Any, Article]: ...", "kind": 2, "label": "in_bulk", "sortText": "0.9999.in_bulk"}, {"detail": "def intersection(\n self: QuerySet[Article, Article],\n *other_qs: QuerySet[Model, Any]\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "intersection", "sortText": "0.9999.intersection"}, {"detail": "def iterator(\n self : QuerySet[Article, Article],\n chunk_size: int | None = None\n) -> Iterator[Article]: ...", "kind": 2, "label": "iterator", "sortText": "0.9999.iterator"}, {"detail": "def last(self: QuerySet[Article, Article]) -> Article | None: ...", "kind": 2, "label": "last", "sortText": "0.9999.last"}, {"detail": "def latest(\n self: QuerySet[Article, Article],\n *fields: OrderBy | str\n) -> Article: ...", "kind": 2, "label": "latest", "sortText": "0.9999.latest"}, {"detail": "type[Article]", "kind": 5, "label": "model", "sortText": "0.9999.model"}, {"detail": "def none(self: QuerySet[Article, Article]) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "none", "sortText": "0.9999.none"}, {"detail": "def only(\n self: QuerySet[Article, Article],\n *fields: str\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "only", "sortText": "0.9999.only"}, {"detail": "def order_by(\n self: QuerySet[Article, Article],\n *field_names: _OrderByFieldName\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "order_by", "sortText": "0.9999.order_by"}, {"detail": "bool", "kind": 5, "label": "ordered", "sortText": "0.9999.ordered"}, {"detail": "\n@overload\ndef prefetch_related(\n self: QuerySet[Article, Article],\n clear: None,\n /\n) -> QuerySet[Article, Article]: ...\ndef prefetch_related[_LookupT: str, _PrefetchedQuerySetT: QuerySet[Model, Model] = QuerySet[Model, Model], _ToAttrT: str = str](\n self: QuerySet[Article, Article],\n *lookups: Prefetch[_LookupT, _PrefetchedQuerySetT, _ToAttrT] | str\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "prefetch_related", "sortText": "0.9999.prefetch_related"}, {"detail": "Query", "kind": 5, "label": "query", "sortText": "0.9999.query"}, {"detail": "def raw(\n self : QuerySet[Article, Article],\n raw_query : _ExecuteQuery,\n params : Any = ...,\n translations: dict[str, str] | None = None,\n using : str | None = None\n) -> RawQuerySet[Article]: ...", "kind": 2, "label": "raw", "sortText": "0.9999.raw"}, {"detail": "def resolve_expression(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> Any: ...", "kind": 2, "label": "resolve_expression", "sortText": "0.9999.resolve_expression"}, {"detail": "def reverse(self: QuerySet[Article, Article]) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "reverse", "sortText": "0.9999.reverse"}, {"detail": "def select_for_update(\n self : QuerySet[Article, Article],\n nowait : bool = False,\n skip_locked: bool = False,\n of : Sequence[str] = ...,\n no_key : bool = False\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "select_for_update", "sortText": "0.9999.select_for_update"}, {"detail": "\n@overload\ndef select_related(\n self: QuerySet[Article, Article],\n clear: None,\n /\n) -> QuerySet[Article, Article]: ...\ndef select_related(\n self: QuerySet[Article, Article],\n *fields: str\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "select_related", "sortText": "0.9999.select_related"}, {"detail": "def union(\n self : QuerySet[Article, Article],\n *other_qs: QuerySet[Model, Any],\n *,\n all : bool = False\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "union", "sortText": "0.9999.union"}, {"detail": "def update(\n self: QuerySet[Article, Article],\n **kwargs: Any\n) -> int: ...", "kind": 2, "label": "update", "sortText": "0.9999.update"}, {"detail": "def update_or_create(\n self : QuerySet[Article, Article],\n defaults : Mapping[str, Any] | None = None,\n create_defaults: Mapping[str, Any] | None = None,\n **kwargs : Any\n) -> tuple[Article, bool]: ...", "kind": 2, "label": "update_or_create", "sortText": "0.9999.update_or_create"}, {"detail": "def using(\n self: QuerySet[Article, Article],\n alias: str | None\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "using", "sortText": "0.9999.using"}, {"detail": "def values(\n self: QuerySet[Article, Article],\n *fields: Combinable | str,\n **expressions: Any\n) -> QuerySet[Article, dict[str, Any]]: ...", "kind": 2, "label": "values", "sortText": "0.9999.values"}, {"detail": "def values_list(\n self : QuerySet[Article, Article],\n *fields: Combinable | str,\n *,\n flat : bool = False,\n named : bool = False\n) -> QuerySet[Article, Any]: ...", "kind": 2, "label": "values_list", "sortText": "0.9999.values_list"}, {"detail": "def _fetch_all(self: QuerySet[Article, Article]) -> None: ...", "kind": 2, "label": "_fetch_all", "sortText": "2.9999._fetch_all"}, {"detail": "type[BaseIterable[Unknown]]", "kind": 5, "label": "_iterable_class", "sortText": "2.9999._iterable_class"}, {"detail": "def _raw_delete(\n self: QuerySet[Article, Article],\n using: str | None\n) -> int: ...", "kind": 2, "label": "_raw_delete", "sortText": "2.9999._raw_delete"}, {"detail": "list[Article] | None", "kind": 5, "label": "_result_cache", "sortText": "2.9999._result_cache"}, {"detail": "def __aiter__(self: QuerySet[Article, Article]) -> AsyncIterator[Article]: ...", "kind": 2, "label": "__aiter__", "sortText": "3.9999.__aiter__"}, {"detail": "def __and__(\n self: QuerySet[Article, Article],\n other: QuerySet[Article, Article]\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "__and__", "sortText": "3.9999.__and__"}, {"detail": "def __bool__(self: QuerySet[Article, Article]) -> bool: ...", "kind": 2, "label": "__bool__", "sortText": "3.9999.__bool__"}, {"detail": "def __class_getitem__(\n cls: type[QuerySet[Article, Article]],\n item: type[Article]\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "__class_getitem__", "sortText": "3.9999.__class_getitem__"}, {"detail": "\n@overload\ndef __getitem__(\n self: QuerySet[Article, Article],\n i: int\n) -> Article: ...\ndef __getitem__(\n self: QuerySet[Article, Article],\n s: slice[Any, Any, Any]\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "__getitem__", "sortText": "3.9999.__getitem__"}, {"detail": "def __getstate__(self: QuerySet[Article, Article]) -> dict[str, Any]: ...", "kind": 2, "label": "__getstate__", "sortText": "3.9999.__getstate__"}, {"detail": "def __init__(\n self : QuerySet[Article, Article],\n model: type[Model] | None = None,\n query: Query | None = None,\n using: str | None = None,\n hints: dict[str, Model] | None = None\n) -> None: ...", "kind": 2, "label": "__init__", "sortText": "3.9999.__init__"}, {"detail": "def __iter__(self: QuerySet[Article, Article]) -> Iterator[Article]: ...", "kind": 2, "label": "__iter__", "sortText": "3.9999.__iter__"}, {"detail": "def __len__(self: QuerySet[Article, Article]) -> int: ...", "kind": 2, "label": "__len__", "sortText": "3.9999.__len__"}, {"detail": "def __or__(\n self: QuerySet[Article, Article],\n other: QuerySet[Article, Article]\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "__or__", "sortText": "3.9999.__or__"}]}} {"suite": "django", "label": "edit queryset then complete (edit+completion)", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 20, "character": 48, "iteration": 5, "result": {"isIncomplete": false, "items": [{"detail": "def aaggregate(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> Coroutine[Unknown, Unknown, dict[str, Any]]: ...", "kind": 2, "label": "aaggregate", "sortText": "0.9999.aaggregate"}, {"detail": "def abulk_create(\n self : QuerySet[Article, Article],\n objs : Iterable[Article],\n batch_size : int | None = None,\n ignore_conflicts: bool = False,\n update_conflicts: bool = False,\n update_fields : Collection[str] | None = None,\n unique_fields : Collection[str] | None = None\n) -> Coroutine[Unknown, Unknown, list[Article]]: ...", "kind": 2, "label": "abulk_create", "sortText": "0.9999.abulk_create"}, {"detail": "def abulk_update(\n self : QuerySet[Article, Article],\n objs : Iterable[Article],\n fields : Iterable[str],\n batch_size: int | None = None\n) -> Coroutine[Unknown, Unknown, int]: ...", "kind": 2, "label": "abulk_update", "sortText": "0.9999.abulk_update"}, {"detail": "def acontains(\n self: QuerySet[Article, Article],\n obj: Model\n) -> Coroutine[Unknown, Unknown, bool]: ...", "kind": 2, "label": "acontains", "sortText": "0.9999.acontains"}, {"detail": "def acount(self: QuerySet[Article, Article]) -> Coroutine[Unknown, Unknown, int]: ...", "kind": 2, "label": "acount", "sortText": "0.9999.acount"}, {"detail": "def acreate(\n self: QuerySet[Article, Article],\n **kwargs: Any\n) -> Coroutine[Unknown, Unknown, Article]: ...", "kind": 2, "label": "acreate", "sortText": "0.9999.acreate"}, {"detail": "def adelete(self: QuerySet[Article, Article]) -> Coroutine[Unknown, Unknown, tuple[int, dict[str, int]]]: ...", "kind": 2, "label": "adelete", "sortText": "0.9999.adelete"}, {"detail": "def aearliest(\n self: QuerySet[Article, Article],\n *fields: OrderBy | str\n) -> Coroutine[Unknown, Unknown, Article]: ...", "kind": 2, "label": "aearliest", "sortText": "0.9999.aearliest"}, {"detail": "def aexists(self: QuerySet[Article, Article]) -> Coroutine[Unknown, Unknown, bool]: ...", "kind": 2, "label": "aexists", "sortText": "0.9999.aexists"}, {"detail": "def aexplain(\n self : QuerySet[Article, Article],\n *,\n format : str | None = None,\n **options: Any\n) -> Coroutine[Unknown, Unknown, str]: ...", "kind": 2, "label": "aexplain", "sortText": "0.9999.aexplain"}, {"detail": "def afirst(self: QuerySet[Article, Article]) -> Coroutine[Unknown, Unknown, Article | None]: ...", "kind": 2, "label": "afirst", "sortText": "0.9999.afirst"}, {"detail": "def aget(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> Coroutine[Unknown, Unknown, Article]: ...", "kind": 2, "label": "aget", "sortText": "0.9999.aget"}, {"detail": "def aget_or_create(\n self : QuerySet[Article, Article],\n defaults: Mapping[str, Any] | None = None,\n **kwargs: Any\n) -> Coroutine[Unknown, Unknown, tuple[Article, bool]]: ...", "kind": 2, "label": "aget_or_create", "sortText": "0.9999.aget_or_create"}, {"detail": "def aggregate(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> dict[str, Any]: ...", "kind": 2, "label": "aggregate", "sortText": "0.9999.aggregate"}, {"detail": "def ain_bulk(\n self : QuerySet[Article, Article],\n id_list : Iterable[Any] | None = None,\n *,\n field_name: str = 'pk'\n) -> Coroutine[Unknown, Unknown, dict[Any, Article]]: ...", "kind": 2, "label": "ain_bulk", "sortText": "0.9999.ain_bulk"}, {"detail": "def aiterator(\n self : QuerySet[Article, Article],\n chunk_size: int = 2000\n) -> AsyncIterator[Article]: ...", "kind": 2, "label": "aiterator", "sortText": "0.9999.aiterator"}, {"detail": "def alast(self: QuerySet[Article, Article]) -> Coroutine[Unknown, Unknown, Article | None]: ...", "kind": 2, "label": "alast", "sortText": "0.9999.alast"}, {"detail": "def alatest(\n self: QuerySet[Article, Article],\n *fields: OrderBy | str\n) -> Coroutine[Unknown, Unknown, Article]: ...", "kind": 2, "label": "alatest", "sortText": "0.9999.alatest"}, {"detail": "def alias(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "alias", "sortText": "0.9999.alias"}, {"detail": "def all(self: QuerySet[Article, Article]) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "all", "sortText": "0.9999.all"}, {"detail": "def annotate(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "annotate", "sortText": "0.9999.annotate"}, {"detail": "def as_manager(cls: type[QuerySet[Article, Article]]) -> Manager[Article]: ...", "kind": 2, "label": "as_manager", "sortText": "0.9999.as_manager"}, {"detail": "def aupdate(\n self: QuerySet[Article, Article],\n **kwargs: Any\n) -> Coroutine[Unknown, Unknown, int]: ...", "kind": 2, "label": "aupdate", "sortText": "0.9999.aupdate"}, {"detail": "def aupdate_or_create(\n self : QuerySet[Article, Article],\n defaults : Mapping[str, Any] | None = None,\n create_defaults: Mapping[str, Any] | None = None,\n **kwargs : Any\n) -> Coroutine[Unknown, Unknown, tuple[Article, bool]]: ...", "kind": 2, "label": "aupdate_or_create", "sortText": "0.9999.aupdate_or_create"}, {"detail": "def bulk_create(\n self : QuerySet[Article, Article],\n objs : Iterable[Article],\n batch_size : int | None = None,\n ignore_conflicts: bool = False,\n update_conflicts: bool = False,\n update_fields : Collection[str] | None = None,\n unique_fields : Collection[str] | None = None\n) -> list[Article]: ...", "kind": 2, "label": "bulk_create", "sortText": "0.9999.bulk_create"}, {"detail": "def bulk_update(\n self : QuerySet[Article, Article],\n objs : Iterable[Article],\n fields : Iterable[str],\n batch_size: int | None = None\n) -> int: ...", "kind": 2, "label": "bulk_update", "sortText": "0.9999.bulk_update"}, {"detail": "def complex_filter(\n self: QuerySet[Article, Article],\n filter_obj: Any\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "complex_filter", "sortText": "0.9999.complex_filter"}, {"detail": "def contains(\n self: QuerySet[Article, Article],\n obj: Model\n) -> bool: ...", "kind": 2, "label": "contains", "sortText": "0.9999.contains"}, {"detail": "def count(self: QuerySet[Article, Article]) -> int: ...", "kind": 2, "label": "count", "sortText": "0.9999.count"}, {"detail": "def create(\n self: QuerySet[Article, Article],\n **kwargs: Any\n) -> Article: ...", "kind": 2, "label": "create", "sortText": "0.9999.create"}, {"detail": "def dates(\n self : QuerySet[Article, Article],\n field_name: str,\n kind : Literal['day', 'month', 'week', 'year'],\n order : Literal['ASC', 'DESC'] = 'ASC'\n) -> QuerySet[Article, date]: ...", "kind": 2, "label": "dates", "sortText": "0.9999.dates"}, {"detail": "def datetimes(\n self : QuerySet[Article, Article],\n field_name: str,\n kind : Literal['day', 'hour', 'minute', 'month', 'second', 'week', 'year'],\n order : Literal['ASC', 'DESC'] = 'ASC',\n tzinfo : tzinfo | None = None\n) -> QuerySet[Article, datetime]: ...", "kind": 2, "label": "datetimes", "sortText": "0.9999.datetimes"}, {"detail": "str", "kind": 5, "label": "db", "sortText": "0.9999.db"}, {"detail": "\n@overload\ndef defer(\n self: QuerySet[Article, Article],\n clear: None,\n /\n) -> QuerySet[Article, Article]: ...\ndef defer(\n self: QuerySet[Article, Article],\n *fields: str\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "defer", "sortText": "0.9999.defer"}, {"detail": "def delete(self: QuerySet[Article, Article]) -> tuple[int, dict[str, int]]: ...", "kind": 2, "label": "delete", "sortText": "0.9999.delete"}, {"detail": "def difference(\n self: QuerySet[Article, Article],\n *other_qs: QuerySet[Model, Any]\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "difference", "sortText": "0.9999.difference"}, {"detail": "def distinct(\n self: QuerySet[Article, Article],\n *field_names: str\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "distinct", "sortText": "0.9999.distinct"}, {"detail": "def earliest(\n self: QuerySet[Article, Article],\n *fields: OrderBy | str\n) -> Article: ...", "kind": 2, "label": "earliest", "sortText": "0.9999.earliest"}, {"detail": "def exclude(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "exclude", "sortText": "0.9999.exclude"}, {"detail": "def exists(self: QuerySet[Article, Article]) -> bool: ...", "kind": 2, "label": "exists", "sortText": "0.9999.exists"}, {"detail": "def explain(\n self : QuerySet[Article, Article],\n *,\n format : str | None = None,\n **options: Any\n) -> str: ...", "kind": 2, "label": "explain", "sortText": "0.9999.explain"}, {"detail": "def extra(\n self : QuerySet[Article, Article],\n select : dict[str, Any] | None = None,\n where : Sequence[str] | None = None,\n params : Sequence[Any] | None = None,\n tables : Sequence[str] | None = None,\n order_by : Sequence[_OrderByFieldName] | None = None,\n select_params: Sequence[Any] | None = None\n) -> QuerySet[Any, Any]: ...", "kind": 2, "label": "extra", "sortText": "0.9999.extra"}, {"detail": "def filter(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "filter", "sortText": "0.9999.filter"}, {"detail": "def first(self: QuerySet[Article, Article]) -> Article | None: ...", "kind": 2, "label": "first", "sortText": "0.9999.first"}, {"detail": "def get(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> Article: ...", "kind": 2, "label": "get", "sortText": "0.9999.get"}, {"detail": "def get_or_create(\n self : QuerySet[Article, Article],\n defaults: Mapping[str, Any] | None = None,\n **kwargs: Any\n) -> tuple[Article, bool]: ...", "kind": 2, "label": "get_or_create", "sortText": "0.9999.get_or_create"}, {"detail": "def in_bulk(\n self : QuerySet[Article, Article],\n id_list : Iterable[Any] | None = None,\n *,\n field_name: str = 'pk'\n) -> dict[Any, Article]: ...", "kind": 2, "label": "in_bulk", "sortText": "0.9999.in_bulk"}, {"detail": "def intersection(\n self: QuerySet[Article, Article],\n *other_qs: QuerySet[Model, Any]\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "intersection", "sortText": "0.9999.intersection"}, {"detail": "def iterator(\n self : QuerySet[Article, Article],\n chunk_size: int | None = None\n) -> Iterator[Article]: ...", "kind": 2, "label": "iterator", "sortText": "0.9999.iterator"}, {"detail": "def last(self: QuerySet[Article, Article]) -> Article | None: ...", "kind": 2, "label": "last", "sortText": "0.9999.last"}, {"detail": "def latest(\n self: QuerySet[Article, Article],\n *fields: OrderBy | str\n) -> Article: ...", "kind": 2, "label": "latest", "sortText": "0.9999.latest"}, {"detail": "type[Article]", "kind": 5, "label": "model", "sortText": "0.9999.model"}, {"detail": "def none(self: QuerySet[Article, Article]) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "none", "sortText": "0.9999.none"}, {"detail": "def only(\n self: QuerySet[Article, Article],\n *fields: str\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "only", "sortText": "0.9999.only"}, {"detail": "def order_by(\n self: QuerySet[Article, Article],\n *field_names: _OrderByFieldName\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "order_by", "sortText": "0.9999.order_by"}, {"detail": "bool", "kind": 5, "label": "ordered", "sortText": "0.9999.ordered"}, {"detail": "\n@overload\ndef prefetch_related(\n self: QuerySet[Article, Article],\n clear: None,\n /\n) -> QuerySet[Article, Article]: ...\ndef prefetch_related[_LookupT: str, _PrefetchedQuerySetT: QuerySet[Model, Model] = QuerySet[Model, Model], _ToAttrT: str = str](\n self: QuerySet[Article, Article],\n *lookups: Prefetch[_LookupT, _PrefetchedQuerySetT, _ToAttrT] | str\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "prefetch_related", "sortText": "0.9999.prefetch_related"}, {"detail": "Query", "kind": 5, "label": "query", "sortText": "0.9999.query"}, {"detail": "def raw(\n self : QuerySet[Article, Article],\n raw_query : _ExecuteQuery,\n params : Any = ...,\n translations: dict[str, str] | None = None,\n using : str | None = None\n) -> RawQuerySet[Article]: ...", "kind": 2, "label": "raw", "sortText": "0.9999.raw"}, {"detail": "def resolve_expression(\n self: QuerySet[Article, Article],\n *args: Any,\n **kwargs: Any\n) -> Any: ...", "kind": 2, "label": "resolve_expression", "sortText": "0.9999.resolve_expression"}, {"detail": "def reverse(self: QuerySet[Article, Article]) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "reverse", "sortText": "0.9999.reverse"}, {"detail": "def select_for_update(\n self : QuerySet[Article, Article],\n nowait : bool = False,\n skip_locked: bool = False,\n of : Sequence[str] = ...,\n no_key : bool = False\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "select_for_update", "sortText": "0.9999.select_for_update"}, {"detail": "\n@overload\ndef select_related(\n self: QuerySet[Article, Article],\n clear: None,\n /\n) -> QuerySet[Article, Article]: ...\ndef select_related(\n self: QuerySet[Article, Article],\n *fields: str\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "select_related", "sortText": "0.9999.select_related"}, {"detail": "def union(\n self : QuerySet[Article, Article],\n *other_qs: QuerySet[Model, Any],\n *,\n all : bool = False\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "union", "sortText": "0.9999.union"}, {"detail": "def update(\n self: QuerySet[Article, Article],\n **kwargs: Any\n) -> int: ...", "kind": 2, "label": "update", "sortText": "0.9999.update"}, {"detail": "def update_or_create(\n self : QuerySet[Article, Article],\n defaults : Mapping[str, Any] | None = None,\n create_defaults: Mapping[str, Any] | None = None,\n **kwargs : Any\n) -> tuple[Article, bool]: ...", "kind": 2, "label": "update_or_create", "sortText": "0.9999.update_or_create"}, {"detail": "def using(\n self: QuerySet[Article, Article],\n alias: str | None\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "using", "sortText": "0.9999.using"}, {"detail": "def values(\n self: QuerySet[Article, Article],\n *fields: Combinable | str,\n **expressions: Any\n) -> QuerySet[Article, dict[str, Any]]: ...", "kind": 2, "label": "values", "sortText": "0.9999.values"}, {"detail": "def values_list(\n self : QuerySet[Article, Article],\n *fields: Combinable | str,\n *,\n flat : bool = False,\n named : bool = False\n) -> QuerySet[Article, Any]: ...", "kind": 2, "label": "values_list", "sortText": "0.9999.values_list"}, {"detail": "def _fetch_all(self: QuerySet[Article, Article]) -> None: ...", "kind": 2, "label": "_fetch_all", "sortText": "2.9999._fetch_all"}, {"detail": "type[BaseIterable[Unknown]]", "kind": 5, "label": "_iterable_class", "sortText": "2.9999._iterable_class"}, {"detail": "def _raw_delete(\n self: QuerySet[Article, Article],\n using: str | None\n) -> int: ...", "kind": 2, "label": "_raw_delete", "sortText": "2.9999._raw_delete"}, {"detail": "list[Article] | None", "kind": 5, "label": "_result_cache", "sortText": "2.9999._result_cache"}, {"detail": "def __aiter__(self: QuerySet[Article, Article]) -> AsyncIterator[Article]: ...", "kind": 2, "label": "__aiter__", "sortText": "3.9999.__aiter__"}, {"detail": "def __and__(\n self: QuerySet[Article, Article],\n other: QuerySet[Article, Article]\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "__and__", "sortText": "3.9999.__and__"}, {"detail": "def __bool__(self: QuerySet[Article, Article]) -> bool: ...", "kind": 2, "label": "__bool__", "sortText": "3.9999.__bool__"}, {"detail": "def __class_getitem__(\n cls: type[QuerySet[Article, Article]],\n item: type[Article]\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "__class_getitem__", "sortText": "3.9999.__class_getitem__"}, {"detail": "\n@overload\ndef __getitem__(\n self: QuerySet[Article, Article],\n i: int\n) -> Article: ...\ndef __getitem__(\n self: QuerySet[Article, Article],\n s: slice[Any, Any, Any]\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "__getitem__", "sortText": "3.9999.__getitem__"}, {"detail": "def __getstate__(self: QuerySet[Article, Article]) -> dict[str, Any]: ...", "kind": 2, "label": "__getstate__", "sortText": "3.9999.__getstate__"}, {"detail": "def __init__(\n self : QuerySet[Article, Article],\n model: type[Model] | None = None,\n query: Query | None = None,\n using: str | None = None,\n hints: dict[str, Model] | None = None\n) -> None: ...", "kind": 2, "label": "__init__", "sortText": "3.9999.__init__"}, {"detail": "def __iter__(self: QuerySet[Article, Article]) -> Iterator[Article]: ...", "kind": 2, "label": "__iter__", "sortText": "3.9999.__iter__"}, {"detail": "def __len__(self: QuerySet[Article, Article]) -> int: ...", "kind": 2, "label": "__len__", "sortText": "3.9999.__len__"}, {"detail": "def __or__(\n self: QuerySet[Article, Article],\n other: QuerySet[Article, Article]\n) -> QuerySet[Article, Article]: ...", "kind": 2, "label": "__or__", "sortText": "3.9999.__or__"}]}} -{"suite": "django", "label": "edit queryset then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 20, "character": 30, "iteration": 1, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) order_by: def order_by(\n self: Manager[Article],\n *field_names: Combinable | str\n) -> QuerySet[Article, Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [Manager](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/manager.pyi#L38,7) | [Combinable](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/expressions.pyi#L23,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [QuerySet](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/query.pyi#L58,7)"}}} -{"suite": "django", "label": "edit queryset then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 20, "character": 30, "iteration": 2, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) order_by: def order_by(\n self: Manager[Article],\n *field_names: Combinable | str\n) -> QuerySet[Article, Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [Manager](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/manager.pyi#L38,7) | [Combinable](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/expressions.pyi#L23,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [QuerySet](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/query.pyi#L58,7)"}}} -{"suite": "django", "label": "edit queryset then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 20, "character": 30, "iteration": 3, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) order_by: def order_by(\n self: Manager[Article],\n *field_names: Combinable | str\n) -> QuerySet[Article, Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [Manager](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/manager.pyi#L38,7) | [Combinable](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/expressions.pyi#L23,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [QuerySet](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/query.pyi#L58,7)"}}} -{"suite": "django", "label": "edit queryset then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 20, "character": 30, "iteration": 4, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) order_by: def order_by(\n self: Manager[Article],\n *field_names: Combinable | str\n) -> QuerySet[Article, Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [Manager](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/manager.pyi#L38,7) | [Combinable](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/expressions.pyi#L23,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [QuerySet](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/query.pyi#L58,7)"}}} -{"suite": "django", "label": "edit queryset then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 20, "character": 30, "iteration": 5, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) order_by: def order_by(\n self: Manager[Article],\n *field_names: Combinable | str\n) -> QuerySet[Article, Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [Manager](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/manager.pyi#L38,7) | [Combinable](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/expressions.pyi#L23,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [QuerySet](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/query.pyi#L58,7)"}}} +{"suite": "django", "label": "edit queryset then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 20, "character": 30, "iteration": 1, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) order_by: def order_by(\n self: Manager[Article],\n *field_names: Combinable | str\n) -> QuerySet[Article, Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [Manager](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/manager.pyi#L38,7) | [Combinable](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/expressions.pyi#L23,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [QuerySet](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/query.pyi#L58,7)"}}} +{"suite": "django", "label": "edit queryset then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 20, "character": 30, "iteration": 2, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) order_by: def order_by(\n self: Manager[Article],\n *field_names: Combinable | str\n) -> QuerySet[Article, Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [Manager](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/manager.pyi#L38,7) | [Combinable](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/expressions.pyi#L23,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [QuerySet](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/query.pyi#L58,7)"}}} +{"suite": "django", "label": "edit queryset then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 20, "character": 30, "iteration": 3, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) order_by: def order_by(\n self: Manager[Article],\n *field_names: Combinable | str\n) -> QuerySet[Article, Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [Manager](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/manager.pyi#L38,7) | [Combinable](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/expressions.pyi#L23,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [QuerySet](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/query.pyi#L58,7)"}}} +{"suite": "django", "label": "edit queryset then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 20, "character": 30, "iteration": 4, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) order_by: def order_by(\n self: Manager[Article],\n *field_names: Combinable | str\n) -> QuerySet[Article, Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [Manager](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/manager.pyi#L38,7) | [Combinable](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/expressions.pyi#L23,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [QuerySet](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/query.pyi#L58,7)"}}} +{"suite": "django", "label": "edit queryset then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py", "line": 20, "character": 30, "iteration": 5, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) order_by: def order_by(\n self: Manager[Article],\n *field_names: Combinable | str\n) -> QuerySet[Article, Article]: ...\n```\n\nGo to [Article](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/src/blog.py#L8,7) | [Manager](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/manager.pyi#L38,7) | [Combinable](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/expressions.pyi#L23,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [QuerySet](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/django/.venv/lib/python3.12/site-packages/django-stubs/db/models/query.pyi#L58,7)"}}} {"suite": "pandas", "label": "report dataframe completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 17, "character": 20, "iteration": 1, "result": {"isIncomplete": true, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"detail": "DataFrame", "kind": 6, "label": "report", "sortText": "0.9999.report"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}, {"detail": "() -> DataFrame", "kind": 3, "label": "build_report", "sortText": "0z.9999.build_report"}]}} {"suite": "pandas", "label": "report dataframe completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 17, "character": 20, "iteration": 2, "result": {"isIncomplete": true, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"detail": "DataFrame", "kind": 6, "label": "report", "sortText": "0.9999.report"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}, {"detail": "() -> DataFrame", "kind": 3, "label": "build_report", "sortText": "0z.9999.build_report"}]}} {"suite": "pandas", "label": "report dataframe completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 17, "character": 20, "iteration": 3, "result": {"isIncomplete": true, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"detail": "DataFrame", "kind": 6, "label": "report", "sortText": "0.9999.report"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}, {"detail": "() -> DataFrame", "kind": 3, "label": "build_report", "sortText": "0z.9999.build_report"}]}} {"suite": "pandas", "label": "report dataframe completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 17, "character": 20, "iteration": 4, "result": {"isIncomplete": true, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"detail": "DataFrame", "kind": 6, "label": "report", "sortText": "0.9999.report"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}, {"detail": "() -> DataFrame", "kind": 3, "label": "build_report", "sortText": "0z.9999.build_report"}]}} {"suite": "pandas", "label": "report dataframe completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 17, "character": 20, "iteration": 5, "result": {"isIncomplete": true, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"detail": "DataFrame", "kind": 6, "label": "report", "sortText": "0.9999.report"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}, {"detail": "() -> DataFrame", "kind": 3, "label": "build_report", "sortText": "0z.9999.build_report"}]}} -{"suite": "pandas", "label": "dataframe groupby hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 11, "character": 20, "iteration": 1, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) groupby: def groupby(\n self : DataFrame,\n by : Scalar,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Scalar, Literal[True]]: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/frame.pyi#L396,7) | [Timedelta](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_libs/tslibs/timedeltas.pyi#L93,7) | [Timestamp](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_libs/tslibs/timestamps.pyi#L51,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1902,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L932,7) | [complexfloating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6722,7) | [date](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/datetime.pyi#L73,7) | [datetime](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/datetime.pyi#L441,7) | [datetime64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L7244,7) | [float](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L735,7) | [floating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6418,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [integer](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L5878,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [timedelta](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/datetime.pyi#L337,7) | [timedelta64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6880,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L555,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [_NoDefault](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_libs/lib.pyi#L11,7) | [DataFrameGroupBy](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/groupby/generic.pyi#L228,7)"}}} -{"suite": "pandas", "label": "dataframe groupby hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 11, "character": 20, "iteration": 2, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) groupby: def groupby(\n self : DataFrame,\n by : Scalar,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Scalar, Literal[True]]: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/frame.pyi#L396,7) | [Timedelta](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_libs/tslibs/timedeltas.pyi#L93,7) | [Timestamp](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_libs/tslibs/timestamps.pyi#L51,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1902,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L932,7) | [complexfloating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6722,7) | [date](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/datetime.pyi#L73,7) | [datetime](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/datetime.pyi#L441,7) | [datetime64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L7244,7) | [float](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L735,7) | [floating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6418,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [integer](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L5878,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [timedelta](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/datetime.pyi#L337,7) | [timedelta64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6880,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L555,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [_NoDefault](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_libs/lib.pyi#L11,7) | [DataFrameGroupBy](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/groupby/generic.pyi#L228,7)"}}} -{"suite": "pandas", "label": "dataframe groupby hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 11, "character": 20, "iteration": 3, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) groupby: def groupby(\n self : DataFrame,\n by : Scalar,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Scalar, Literal[True]]: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/frame.pyi#L396,7) | [Timedelta](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_libs/tslibs/timedeltas.pyi#L93,7) | [Timestamp](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_libs/tslibs/timestamps.pyi#L51,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1902,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L932,7) | [complexfloating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6722,7) | [date](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/datetime.pyi#L73,7) | [datetime](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/datetime.pyi#L441,7) | [datetime64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L7244,7) | [float](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L735,7) | [floating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6418,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [integer](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L5878,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [timedelta](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/datetime.pyi#L337,7) | [timedelta64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6880,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L555,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [_NoDefault](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_libs/lib.pyi#L11,7) | [DataFrameGroupBy](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/groupby/generic.pyi#L228,7)"}}} -{"suite": "pandas", "label": "dataframe groupby hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 11, "character": 20, "iteration": 4, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) groupby: def groupby(\n self : DataFrame,\n by : Scalar,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Scalar, Literal[True]]: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/frame.pyi#L396,7) | [Timedelta](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_libs/tslibs/timedeltas.pyi#L93,7) | [Timestamp](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_libs/tslibs/timestamps.pyi#L51,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1902,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L932,7) | [complexfloating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6722,7) | [date](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/datetime.pyi#L73,7) | [datetime](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/datetime.pyi#L441,7) | [datetime64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L7244,7) | [float](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L735,7) | [floating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6418,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [integer](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L5878,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [timedelta](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/datetime.pyi#L337,7) | [timedelta64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6880,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L555,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [_NoDefault](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_libs/lib.pyi#L11,7) | [DataFrameGroupBy](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/groupby/generic.pyi#L228,7)"}}} -{"suite": "pandas", "label": "dataframe groupby hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 11, "character": 20, "iteration": 5, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) groupby: def groupby(\n self : DataFrame,\n by : Scalar,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Scalar, Literal[True]]: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/frame.pyi#L396,7) | [Timedelta](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_libs/tslibs/timedeltas.pyi#L93,7) | [Timestamp](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_libs/tslibs/timestamps.pyi#L51,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1902,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L932,7) | [complexfloating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6722,7) | [date](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/datetime.pyi#L73,7) | [datetime](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/datetime.pyi#L441,7) | [datetime64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L7244,7) | [float](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L735,7) | [floating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6418,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [integer](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L5878,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [timedelta](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/datetime.pyi#L337,7) | [timedelta64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6880,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L555,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [_NoDefault](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/_libs/lib.pyi#L11,7) | [DataFrameGroupBy](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/groupby/generic.pyi#L228,7)"}}} +{"suite": "pandas", "label": "dataframe groupby hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 11, "character": 20, "iteration": 1, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) groupby: def groupby(\n self : DataFrame,\n by : Scalar,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Scalar, Literal[True]]: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/frame.pyi#L396,7) | [Timedelta](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_libs/tslibs/timedeltas.pyi#L93,7) | [Timestamp](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_libs/tslibs/timestamps.pyi#L51,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1902,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L932,7) | [complexfloating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6722,7) | [date](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/datetime.pyi#L73,7) | [datetime](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/datetime.pyi#L441,7) | [datetime64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L7244,7) | [float](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L735,7) | [floating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6418,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [integer](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L5878,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [timedelta](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/datetime.pyi#L337,7) | [timedelta64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6880,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L555,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [_NoDefault](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_libs/lib.pyi#L11,7) | [DataFrameGroupBy](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/groupby/generic.pyi#L228,7)"}}} +{"suite": "pandas", "label": "dataframe groupby hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 11, "character": 20, "iteration": 2, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) groupby: def groupby(\n self : DataFrame,\n by : Scalar,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Scalar, Literal[True]]: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/frame.pyi#L396,7) | [Timedelta](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_libs/tslibs/timedeltas.pyi#L93,7) | [Timestamp](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_libs/tslibs/timestamps.pyi#L51,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1902,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L932,7) | [complexfloating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6722,7) | [date](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/datetime.pyi#L73,7) | [datetime](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/datetime.pyi#L441,7) | [datetime64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L7244,7) | [float](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L735,7) | [floating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6418,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [integer](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L5878,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [timedelta](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/datetime.pyi#L337,7) | [timedelta64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6880,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L555,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [_NoDefault](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_libs/lib.pyi#L11,7) | [DataFrameGroupBy](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/groupby/generic.pyi#L228,7)"}}} +{"suite": "pandas", "label": "dataframe groupby hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 11, "character": 20, "iteration": 3, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) groupby: def groupby(\n self : DataFrame,\n by : Scalar,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Scalar, Literal[True]]: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/frame.pyi#L396,7) | [Timedelta](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_libs/tslibs/timedeltas.pyi#L93,7) | [Timestamp](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_libs/tslibs/timestamps.pyi#L51,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1902,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L932,7) | [complexfloating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6722,7) | [date](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/datetime.pyi#L73,7) | [datetime](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/datetime.pyi#L441,7) | [datetime64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L7244,7) | [float](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L735,7) | [floating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6418,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [integer](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L5878,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [timedelta](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/datetime.pyi#L337,7) | [timedelta64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6880,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L555,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [_NoDefault](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_libs/lib.pyi#L11,7) | [DataFrameGroupBy](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/groupby/generic.pyi#L228,7)"}}} +{"suite": "pandas", "label": "dataframe groupby hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 11, "character": 20, "iteration": 4, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) groupby: def groupby(\n self : DataFrame,\n by : Scalar,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Scalar, Literal[True]]: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/frame.pyi#L396,7) | [Timedelta](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_libs/tslibs/timedeltas.pyi#L93,7) | [Timestamp](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_libs/tslibs/timestamps.pyi#L51,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1902,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L932,7) | [complexfloating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6722,7) | [date](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/datetime.pyi#L73,7) | [datetime](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/datetime.pyi#L441,7) | [datetime64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L7244,7) | [float](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L735,7) | [floating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6418,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [integer](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L5878,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [timedelta](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/datetime.pyi#L337,7) | [timedelta64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6880,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L555,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [_NoDefault](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_libs/lib.pyi#L11,7) | [DataFrameGroupBy](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/groupby/generic.pyi#L228,7)"}}} +{"suite": "pandas", "label": "dataframe groupby hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 11, "character": 20, "iteration": 5, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) groupby: def groupby(\n self : DataFrame,\n by : Scalar,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Scalar, Literal[True]]: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/frame.pyi#L396,7) | [Timedelta](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_libs/tslibs/timedeltas.pyi#L93,7) | [Timestamp](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_libs/tslibs/timestamps.pyi#L51,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1902,7) | [complex](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L932,7) | [complexfloating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6722,7) | [date](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/datetime.pyi#L73,7) | [datetime](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/datetime.pyi#L441,7) | [datetime64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L7244,7) | [float](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L735,7) | [floating](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6418,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [integer](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L5878,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [timedelta](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/datetime.pyi#L337,7) | [timedelta64](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/.venv/lib/python3.12/site-packages/numpy/__init__.pyi#L6880,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L555,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [_NoDefault](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/_libs/lib.pyi#L11,7) | [DataFrameGroupBy](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/groupby/generic.pyi#L228,7)"}}} {"suite": "pandas", "label": "build report definition", "method": "textDocument/definition", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 16, "character": 17, "iteration": 1, "result": {"range": {"end": {"character": 16, "line": 3}, "start": {"character": 4, "line": 3}}, "uri": "file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py"}} {"suite": "pandas", "label": "build report definition", "method": "textDocument/definition", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 16, "character": 17, "iteration": 2, "result": {"range": {"end": {"character": 16, "line": 3}, "start": {"character": 4, "line": 3}}, "uri": "file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py"}} {"suite": "pandas", "label": "build report definition", "method": "textDocument/definition", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 16, "character": 17, "iteration": 3, "result": {"range": {"end": {"character": 16, "line": 3}, "start": {"character": 4, "line": 3}}, "uri": "file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py"}} @@ -68,21 +68,21 @@ {"suite": "pandas", "label": "edit dataframe then complete (edit+completion)", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 17, "character": 39, "iteration": 3, "result": {"isIncomplete": false, "items": [{"detail": "str", "kind": 5, "label": "Name", "sortText": "0.9999.Name"}, {"detail": "DataFrame", "kind": 5, "label": "T", "sortText": "0z.9999.T"}, {"detail": "def abs(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "abs", "sortText": "0z.9999.abs"}, {"detail": "def add(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "add", "sortText": "0z.9999.add"}, {"detail": "def add_prefix(\n self : DataFrame,\n prefix: str,\n axis : Literal['columns', 'index', 0, 1] | None = None\n) -> DataFrame: ...", "kind": 2, "label": "add_prefix", "sortText": "0z.9999.add_prefix"}, {"detail": "def add_suffix(\n self : DataFrame,\n suffix: str,\n axis : Literal['columns', 'index', 0, 1] | None = None\n) -> DataFrame: ...", "kind": 2, "label": "add_suffix", "sortText": "0z.9999.add_suffix"}, {"detail": "\n@overload\ndef aggregate(\n self : DataFrame,\n func : ((...) -> Unknown) | Mapping[Unknown, ((...) -> Unknown) | str | ufunc] | str | ufunc,\n axis : Axis = 0,\n **kwargs: Any\n) -> Series: ...\ndef aggregate(\n self : DataFrame,\n func : Mapping[Unknown, ((...) -> Unknown) | list[((...) -> Unknown) | str | ufunc] | str | ufunc] | list[AggFuncTypeBase] | None = ...,\n axis : Axis = 0,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "agg", "sortText": "0z.9999.agg"}, {"detail": "\n@overload\ndef aggregate(\n self : DataFrame,\n func : ((...) -> Unknown) | Mapping[Unknown, ((...) -> Unknown) | str | ufunc] | str | ufunc,\n axis : Axis = 0,\n **kwargs: Any\n) -> Series: ...\ndef aggregate(\n self : DataFrame,\n func : Mapping[Unknown, ((...) -> Unknown) | list[((...) -> Unknown) | str | ufunc] | str | ufunc] | list[AggFuncTypeBase] | None = ...,\n axis : Axis = 0,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "aggregate", "sortText": "0z.9999.aggregate"}, {"detail": "def align[NDFrameT: NDFrame](\n self : DataFrame,\n other : NDFrameT,\n join : AlignJoin = 'outer',\n axis : Literal['columns', 'index', 0, 1] | None = None,\n level : Hashable | None = None,\n copy : bool = True,\n fill_value: NAType | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...\n) -> tuple[DataFrame, NDFrameT]: ...", "kind": 2, "label": "align", "sortText": "0z.9999.align"}, {"detail": "\n@overload\ndef all(\n self : DataFrame,\n axis : None,\n bool_only: builtins.bool | None = ...,\n skipna : builtins.bool = ...,\n **kwargs : Any\n) -> numpy.bool: ...\ndef all(\n self : DataFrame,\n axis : Axis = 0,\n bool_only: builtins.bool | None = ...,\n skipna : builtins.bool = ...,\n **kwargs : Any\n) -> Series[builtins.bool]: ...", "kind": 2, "label": "all", "sortText": "0z.9999.all"}, {"detail": "\n@overload\ndef any(\n self : DataFrame,\n *,\n axis : None,\n bool_only: builtins.bool | None = ...,\n skipna : builtins.bool = ...,\n **kwargs : Any\n) -> numpy.bool: ...\ndef any(\n self : DataFrame,\n *,\n axis : Axis = 0,\n bool_only: builtins.bool | None = ...,\n skipna : builtins.bool = ...,\n **kwargs : Any\n) -> Series[builtins.bool]: ...", "kind": 2, "label": "any", "sortText": "0z.9999.any"}, {"detail": "\n@overload\ndef apply(\n self : DataFrame,\n f : (...) -> Index | MutableSequence[Any] | Series | ndarray[tuple[int]] | tuple[Any, ...],\n axis : AxisIndex = ...,\n raw : bool = ...,\n result_type: None = None,\n args : Any = ...,\n **kwargs : Any\n) -> DataFrame: ...\ndef apply[S2: SeriesDType](\n self : DataFrame,\n f : (...) -> NAType | S2,\n axis : AxisIndex = ...,\n raw : bool = ...,\n result_type: None = None,\n args : Any = ...,\n **kwargs : Any\n) -> Series[S2]: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Mapping[Any, Any],\n axis : AxisIndex = ...,\n raw : bool = ...,\n result_type: None = None,\n args : Any = ...,\n **kwargs : Any\n) -> Series: ...\ndef apply[S2: SeriesDType](\n self : DataFrame,\n f : (...) -> NAType | S2,\n axis : Axis = 0,\n raw : bool = ...,\n args : Any = ...,\n *,\n result_type: Literal['expand', 'reduce'],\n **kwargs : Any\n) -> Series[S2]: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Index | Mapping[Any, Any] | MutableSequence[Any] | Series | ndarray[tuple[int]] | tuple[Any, ...],\n axis : Axis = 0,\n raw : bool = ...,\n args : Any = ...,\n *,\n result_type: Literal['expand'],\n **kwargs : Any\n) -> DataFrame: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Index | Mapping[Any, Any] | MutableSequence[Any] | ndarray[tuple[int]] | tuple[Any, ...],\n axis : Axis = 0,\n raw : bool = ...,\n args : Any = ...,\n *,\n result_type: Literal['reduce'],\n **kwargs : Any\n) -> Series: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Index | Mapping[Any, Any] | MutableSequence[Any] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray[tuple[int]] | str | timedelta | timedelta64 | tuple[Any, ...],\n axis : Axis = 0,\n raw : bool = ...,\n args : Any = ...,\n *,\n result_type: Literal['broadcast'],\n **kwargs : Any\n) -> DataFrame: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Series,\n axis : AxisIndex = 0,\n raw : bool = ...,\n args : Any = ...,\n *,\n result_type: Literal['reduce'],\n **kwargs : Any\n) -> Series: ...\ndef apply[S2: SeriesDType](\n self : DataFrame,\n f : (...) -> NAType | S2,\n raw : bool = ...,\n result_type: None = None,\n args : Any = ...,\n *,\n axis : AxisColumn,\n **kwargs : Any\n) -> Series[S2]: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Index | Mapping[Any, Any] | MutableSequence[Any] | ndarray[tuple[int]] | tuple[Any, ...],\n raw : bool = ...,\n result_type: None = None,\n args : Any = ...,\n *,\n axis : AxisColumn,\n **kwargs : Any\n) -> Series: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Series,\n raw : bool = ...,\n result_type: None = None,\n args : Any = ...,\n *,\n axis : AxisColumn,\n **kwargs : Any\n) -> DataFrame: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Series,\n raw : bool = ...,\n args : Any = ...,\n *,\n axis : AxisColumn,\n result_type: Literal['reduce'],\n **kwargs : Any\n) -> DataFrame: ...", "kind": 2, "label": "apply", "sortText": "0z.9999.apply"}, {"detail": "def asfreq(\n self : DataFrame,\n freq : Frequency,\n method : Literal['backfill', 'bfill', 'ffill', 'pad'] | None = None,\n how : Literal['end', 'start'] | None = ...,\n normalize : bool = False,\n fill_value: Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "asfreq", "sortText": "0z.9999.asfreq"}, {"detail": "def asof(\n self : DataFrame,\n where : ExtensionArray | Index | Sequence[Scalar] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64,\n subset: Hashable | list[Hashable] | None = None\n) -> DataFrame: ...", "kind": 2, "label": "asof", "sortText": "0z.9999.asof"}, {"detail": "def assign(\n self: DataFrame,\n **kwargs: IntoColumn\n) -> DataFrame: ...", "kind": 2, "label": "assign", "sortText": "0z.9999.assign"}, {"detail": "def astype(\n self : DataFrame,\n dtype : Literal[' DataFrame: ...", "kind": 2, "label": "astype", "sortText": "0z.9999.astype"}, {"detail": "_AtIndexerFrame", "kind": 5, "label": "at", "sortText": "0z.9999.at"}, {"detail": "def at_time(\n self: DataFrame,\n time: str | time,\n asof: bool = False,\n axis: Literal['columns', 'index', 0, 1] | None = 0\n) -> DataFrame: ...", "kind": 2, "label": "at_time", "sortText": "0z.9999.at_time"}, {"detail": "dict[Hashable | None, Any]", "kind": 5, "label": "attrs", "sortText": "0z.9999.attrs"}, {"detail": "list[Index]", "kind": 5, "label": "axes", "sortText": "0z.9999.axes"}, {"detail": "def between_time(\n self : DataFrame,\n start_time: str | time,\n end_time : str | time,\n inclusive : IntervalClosedType = 'both',\n axis : Literal['columns', 'index', 0, 1] | None = 0\n) -> DataFrame: ...", "kind": 2, "label": "between_time", "sortText": "0z.9999.between_time"}, {"detail": "\n@overload\ndef bfill(\n self : DataFrame,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n inplace : Literal[True],\n limit : int | None = ...,\n limit_area: Literal['inside', 'outside'] | None = ...\n) -> None: ...\ndef bfill(\n self : DataFrame,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n inplace : Literal[False] = False,\n limit : int | None = ...,\n limit_area: Literal['inside', 'outside'] | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "bfill", "sortText": "0z.9999.bfill"}, {"detail": "def bool(self: DataFrame) -> bool: ...", "kind": 2, "label": "bool", "sortText": "0z.9999.bool"}, {"detail": "\n@overload\ndef boxplot(\n self : DataFrame,\n by : None = None,\n ax : Unknown = None,\n fontsize : float | str | None = None,\n rot : float = 0,\n grid : bool = True,\n figsize : tuple[float, float] | None = None,\n layout : tuple[int, int] | None = None,\n *,\n return_type: Literal['axes'] | None = None,\n backend : str | None = None,\n **kwargs : Any\n) -> Unknown: ...\ndef boxplot(\n self : DataFrame,\n by : None = None,\n ax : Unknown = None,\n fontsize : float | str | None = None,\n rot : float = 0,\n grid : bool = True,\n figsize : tuple[float, float] | None = None,\n layout : tuple[int, int] | None = None,\n *,\n return_type: Literal['dict'],\n backend : str | None = None,\n **kwargs : Any\n) -> dict[str, Unknown]: ...\ndef boxplot(\n self : DataFrame,\n by : None = None,\n ax : Unknown = None,\n fontsize : float | str | None = None,\n rot : float = 0,\n grid : bool = True,\n figsize : tuple[float, float] | None = None,\n layout : tuple[int, int] | None = None,\n *,\n return_type: Literal['both'],\n backend : str | None = None,\n **kwargs : Any\n) -> _BoxPlotT: ...\ndef boxplot(\n self : DataFrame,\n by : Hashable | MutableSequence[Unknown] | ndarray[tuple[int]] | range | tuple[Unknown, ...],\n ax : Unknown = None,\n fontsize : float | str | None = None,\n rot : float = 0,\n grid : bool = True,\n figsize : tuple[float, float] | None = None,\n layout : tuple[int, int] | None = None,\n *,\n return_type: None = None,\n backend : str | None = None,\n **kwargs : Any\n) -> Unknown: ...\ndef boxplot(\n self : DataFrame,\n by : Hashable | MutableSequence[Unknown] | ndarray[tuple[int]] | range | tuple[Unknown, ...],\n ax : Unknown = None,\n fontsize : float | str | None = None,\n rot : float = 0,\n grid : bool = True,\n figsize : tuple[float, float] | None = None,\n layout : tuple[int, int] | None = None,\n *,\n return_type: Literal['axes', 'both', 'dict'],\n backend : str | None = None,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "boxplot", "sortText": "0z.9999.boxplot"}, {"detail": "\n@overload\ndef clip(\n self : DataFrame,\n lower : float | None = ...,\n upper : float | None = ...,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n inplace : Literal[False] = False,\n **kwargs: Any\n) -> DataFrame: ...\ndef clip(\n self : DataFrame,\n lower : AnyArrayLike = ...,\n upper : ExtensionArray | Index | Series | ndarray | None = ...,\n *,\n axis : Axis = ...,\n inplace : Literal[False] = False,\n **kwargs: Any\n) -> DataFrame: ...\ndef clip(\n self : DataFrame,\n lower : ExtensionArray | Index | Series | ndarray | None = ...,\n upper : AnyArrayLike = ...,\n *,\n axis : Axis = ...,\n inplace : Literal[False] = False,\n **kwargs: Any\n) -> DataFrame: ...\ndef clip(\n self : DataFrame,\n lower : None = None,\n upper : None = None,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n inplace : Literal[True],\n **kwargs: Any\n) -> DataFrame: ...\ndef clip(\n self : DataFrame,\n lower : float | None = ...,\n upper : float | None = ...,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n inplace : Literal[True],\n **kwargs: Any\n) -> None: ...\ndef clip(\n self : DataFrame,\n lower : AnyArrayLike = ...,\n upper : ExtensionArray | Index | Series | ndarray | None = ...,\n *,\n axis : Axis = ...,\n inplace : Literal[True],\n **kwargs: Any\n) -> None: ...\ndef clip(\n self : DataFrame,\n lower : ExtensionArray | Index | Series | ndarray | None = ...,\n upper : AnyArrayLike = ...,\n *,\n axis : Axis = ...,\n inplace : Literal[True],\n **kwargs: Any\n) -> None: ...", "kind": 2, "label": "clip", "sortText": "0z.9999.clip"}, {"detail": "Index[str]", "kind": 5, "label": "columns", "sortText": "0z.9999.columns"}, {"detail": "def combine(\n self : DataFrame,\n other : DataFrame,\n func : (...) -> Unknown,\n fill_value: Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = None,\n overwrite : bool = True\n) -> DataFrame: ...", "kind": 2, "label": "combine", "sortText": "0z.9999.combine"}, {"detail": "def combine_first(\n self: DataFrame,\n other: DataFrame\n) -> DataFrame: ...", "kind": 2, "label": "combine_first", "sortText": "0z.9999.combine_first"}, {"detail": "def compare(\n self : DataFrame,\n other : DataFrame,\n align_axis : Axis = 1,\n keep_shape : bool = False,\n keep_equal : bool = False,\n result_names: Suffixes = ...\n) -> DataFrame: ...", "kind": 2, "label": "compare", "sortText": "0z.9999.compare"}, {"detail": "def convert_dtypes(\n self : DataFrame,\n infer_objects : bool = True,\n convert_string : bool = True,\n convert_integer : bool = True,\n convert_boolean : bool = True,\n convert_floating: bool = True,\n dtype_backend : DtypeBackend = 'numpy_nullable'\n) -> DataFrame: ...", "kind": 2, "label": "convert_dtypes", "sortText": "0z.9999.convert_dtypes"}, {"detail": "def copy(\n self: DataFrame,\n deep: bool = True\n) -> DataFrame: ...", "kind": 2, "label": "copy", "sortText": "0z.9999.copy"}, {"detail": "def corr(\n self : DataFrame,\n method : Literal['kendall', 'pearson', 'spearman'] = 'pearson',\n min_periods : int = ...,\n numeric_only: bool = False\n) -> DataFrame: ...", "kind": 2, "label": "corr", "sortText": "0z.9999.corr"}, {"detail": "def corrwith(\n self : DataFrame,\n other : DataFrame | Series,\n axis : Literal['columns', 'index', 0, 1] | None = 0,\n drop : bool = False,\n method : Literal['kendall', 'pearson', 'spearman'] = 'pearson',\n numeric_only: bool = False\n) -> Series: ...", "kind": 2, "label": "corrwith", "sortText": "0z.9999.corrwith"}, {"detail": "def count(\n self : DataFrame,\n axis : Axis = 0,\n numeric_only: bool = False\n) -> Series[int]: ...", "kind": 2, "label": "count", "sortText": "0z.9999.count"}, {"detail": "def cov(\n self : DataFrame,\n min_periods : int | None = None,\n ddof : int = 1,\n numeric_only: bool = False\n) -> DataFrame: ...", "kind": 2, "label": "cov", "sortText": "0z.9999.cov"}, {"detail": "def cummax(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n skipna : bool = True,\n *args : Any,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "cummax", "sortText": "0z.9999.cummax"}, {"detail": "def cummin(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n skipna : bool = True,\n *args : Any,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "cummin", "sortText": "0z.9999.cummin"}, {"detail": "def cumprod(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n skipna : bool = True,\n *args : Any,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "cumprod", "sortText": "0z.9999.cumprod"}, {"detail": "def cumsum(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n skipna : bool = True,\n *args : Any,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "cumsum", "sortText": "0z.9999.cumsum"}, {"detail": "def describe(\n self : DataFrame,\n percentiles: list[float] | None = ...,\n include : Literal['all'] | list[Dtype] | None = ...,\n exclude : list[Dtype] | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "describe", "sortText": "0z.9999.describe"}, {"detail": "def diff(\n self : DataFrame,\n periods: int = 1,\n axis : Axis = 0\n) -> DataFrame: ...", "kind": 2, "label": "diff", "sortText": "0z.9999.diff"}, {"detail": "def truediv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "div", "sortText": "0z.9999.div"}, {"detail": "def truediv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "divide", "sortText": "0z.9999.divide"}, {"detail": "\n@overload\ndef dot(\n self: DataFrame,\n other: DataFrame | ExtensionArray | ndarray\n) -> DataFrame: ...\ndef dot(\n self: DataFrame,\n other: Series\n) -> Series: ...", "kind": 2, "label": "dot", "sortText": "0z.9999.dot"}, {"detail": "\n@overload\ndef drop(\n self : DataFrame,\n labels : Unknown = ...,\n *,\n axis : Unknown = ...,\n index : None,\n columns: Unknown = ...,\n level : Unknown = ...,\n inplace: Unknown = ...,\n errors : Unknown = ...\n) -> Never: ...\ndef drop(\n self : DataFrame,\n labels : Unknown = ...,\n *,\n axis : Unknown = ...,\n index : Unknown = ...,\n columns: None,\n level : Unknown = ...,\n inplace: Unknown = ...,\n errors : Unknown = ...\n) -> Never: ...\ndef drop(\n self : DataFrame,\n labels : None,\n *,\n axis : Unknown = ...,\n index : Unknown = ...,\n columns: Unknown = ...,\n level : Unknown = ...,\n inplace: Unknown = ...,\n errors : Unknown = ...\n) -> Never: ...\ndef drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable] = ...,\n columns: Hashable | Iterable[Hashable],\n level : Hashable | None = ...,\n inplace: Literal[True],\n errors : IgnoreRaise = ...\n) -> None: ...\ndef drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable],\n columns: Hashable | Iterable[Hashable] = ...,\n level : Hashable | None = ...,\n inplace: Literal[True],\n errors : IgnoreRaise = ...\n) -> None: ...\ndef drop(\n self : DataFrame,\n labels : Hashable | Index | Sequence[Hashable],\n *,\n axis : Axis = ...,\n index : None = None,\n columns: None = None,\n level : Hashable | None = ...,\n inplace: Literal[True],\n errors : IgnoreRaise = ...\n) -> None: ...\ndef drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable] = ...,\n columns: Hashable | Iterable[Hashable],\n level : Hashable | None = ...,\n inplace: Literal[False] = False,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...\ndef drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable],\n columns: Hashable | Iterable[Hashable] = ...,\n level : Hashable | None = ...,\n inplace: Literal[False] = False,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...\ndef drop(\n self : DataFrame,\n labels : Hashable | Index | Sequence[Hashable],\n *,\n axis : Axis = ...,\n index : None = None,\n columns: None = None,\n level : Hashable | None = ...,\n inplace: Literal[False] = False,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...", "kind": 2, "label": "drop", "sortText": "0z.9999.drop"}, {"detail": "\n@overload\ndef drop_duplicates(\n self : DataFrame,\n subset : Hashable | Iterable[Hashable] | None = ...,\n *,\n keep : DropKeep = ...,\n inplace : Literal[True],\n ignore_index: bool = ...\n) -> None: ...\ndef drop_duplicates(\n self : DataFrame,\n subset : Hashable | Iterable[Hashable] | None = ...,\n *,\n keep : DropKeep = ...,\n inplace : Literal[False] = False,\n ignore_index: bool = ...\n) -> DataFrame: ...", "kind": 2, "label": "drop_duplicates", "sortText": "0z.9999.drop_duplicates"}, {"detail": "def droplevel(\n self : DataFrame,\n level: Hashable | list[Hashable],\n axis : Axis = 0\n) -> DataFrame: ...", "kind": 2, "label": "droplevel", "sortText": "0z.9999.droplevel"}, {"detail": "\n@overload\ndef dropna(\n self : DataFrame,\n *,\n axis : Axis = 0,\n how : AnyAll = ...,\n thresh : int | None = ...,\n subset : Index | Sequence[Unknown] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray[tuple[int]] | str | timedelta | timedelta64 | None = ...,\n inplace : Literal[True],\n ignore_index: bool = ...\n) -> None: ...\ndef dropna(\n self : DataFrame,\n *,\n axis : Axis = 0,\n how : AnyAll = ...,\n thresh : int | None = ...,\n subset : Index | Sequence[Unknown] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray[tuple[int]] | str | timedelta | timedelta64 | None = ...,\n inplace : Literal[False] = False,\n ignore_index: bool = ...\n) -> DataFrame: ...", "kind": 2, "label": "dropna", "sortText": "0z.9999.dropna"}, {"detail": "Series", "kind": 5, "label": "dtypes", "sortText": "0z.9999.dtypes"}, {"detail": "def duplicated(\n self : DataFrame,\n subset: Hashable | Iterable[Hashable] | None = ...,\n keep : DropKeep = 'first'\n) -> Series: ...", "kind": 2, "label": "duplicated", "sortText": "0z.9999.duplicated"}, {"detail": "bool", "kind": 5, "label": "empty", "sortText": "0z.9999.empty"}, {"detail": "def eq(\n self : DataFrame,\n other: DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level: Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "eq", "sortText": "0z.9999.eq"}, {"detail": "def equals(\n self: DataFrame,\n other: DataFrame | Series\n) -> bool: ...", "kind": 2, "label": "equals", "sortText": "0z.9999.equals"}, {"detail": "\n@overload\ndef eval(\n self: DataFrame,\n expr: str,\n *,\n inplace: Literal[True],\n **kwargs: Any\n) -> None: ...\ndef eval(\n self : DataFrame,\n expr : str,\n *,\n inplace : Literal[False] = False,\n **kwargs: Any\n) -> DataFrame | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64: ...", "kind": 2, "label": "eval", "sortText": "0z.9999.eval"}, {"detail": "def ewm(\n self : DataFrame,\n com : float | None = ...,\n span : float | None = ...,\n halflife : float | None = ...,\n alpha : float | None = ...,\n min_periods: int = 0,\n adjust : bool = True,\n ignore_na : bool = False,\n axis : Axis = 0\n) -> ExponentialMovingWindow[DataFrame]: ...", "kind": 2, "label": "ewm", "sortText": "0z.9999.ewm"}, {"detail": "def expanding(\n self : DataFrame,\n min_periods: int = 1,\n axis : AxisIndex = 0,\n method : CalculationMethod = 'single'\n) -> Expanding[DataFrame]: ...", "kind": 2, "label": "expanding", "sortText": "0z.9999.expanding"}, {"detail": "def explode(\n self : DataFrame,\n column : Sequence[Hashable],\n ignore_index: bool = False\n) -> DataFrame: ...", "kind": 2, "label": "explode", "sortText": "0z.9999.explode"}, {"detail": "\n@overload\ndef ffill(\n self : DataFrame,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n inplace : Literal[True],\n limit : int | None = ...,\n limit_area: Literal['inside', 'outside'] | None = ...\n) -> None: ...\ndef ffill(\n self : DataFrame,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n inplace : Literal[False] = False,\n limit : int | None = ...,\n limit_area: Literal['inside', 'outside'] | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "ffill", "sortText": "0z.9999.ffill"}, {"detail": "\n@overload\ndef fillna(\n self : DataFrame,\n value : DataFrame | NAType | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | dict[Unknown, Unknown] | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n limit : int = ...,\n inplace: Literal[True]\n) -> None: ...\ndef fillna(\n self : DataFrame,\n value : DataFrame | NAType | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | dict[Unknown, Unknown] | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n limit : int = ...,\n inplace: Literal[False] = False\n) -> DataFrame: ...", "kind": 2, "label": "fillna", "sortText": "0z.9999.fillna"}, {"detail": "def filter(\n self : DataFrame,\n items: ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = ...,\n like : str | None = ...,\n regex: str | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = None\n) -> DataFrame: ...", "kind": 2, "label": "filter", "sortText": "0z.9999.filter"}, {"detail": "def first_valid_index(self: DataFrame) -> Scalar: ...", "kind": 2, "label": "first_valid_index", "sortText": "0z.9999.first_valid_index"}, {"detail": "def floordiv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | float | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "floordiv", "sortText": "0z.9999.floordiv"}, {"detail": "\n@overload\ndef from_dict(\n cls : type[DataFrame],\n data : dict[Any, Any],\n orient : Literal['index'],\n dtype : Literal[' DataFrame: ...\ndef from_dict(\n cls : type[DataFrame],\n data : dict[Any, Any],\n orient: Literal['columns', 'tight'] = ...,\n dtype : Literal[' DataFrame: ...", "kind": 2, "label": "from_dict", "sortText": "0z.9999.from_dict"}, {"detail": "def from_records[HashableT: Hashable](\n cls : type[DataFrame],\n data : Iterable[Mapping[HashableT, Any]] | Iterable[SequenceNotStr[Any]] | Mapping[HashableT, SequenceNotStr[Any]] | Mapping[HashableT, Any] | ndarray[tuple[int, int]],\n index : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | str | None = None,\n exclude : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n columns : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n coerce_float: bool = False,\n nrows : int | None = None\n) -> DataFrame: ...", "kind": 2, "label": "from_records", "sortText": "0z.9999.from_records"}, {"detail": "def ge(\n self : DataFrame,\n other: DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level: Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "ge", "sortText": "0z.9999.ge"}, {"detail": "\n@overload\ndef get(\n self : DataFrame,\n key : Hashable,\n default: None = None\n) -> Series | None: ...\ndef get[T](\n self: DataFrame,\n key: Hashable,\n default: T\n) -> Series | T: ...\ndef get(\n self : DataFrame,\n key : list[Hashable],\n default: None = None\n) -> DataFrame | None: ...\ndef get[T](\n self: DataFrame,\n key: list[Hashable],\n default: T\n) -> DataFrame | T: ...", "kind": 2, "label": "get", "sortText": "0z.9999.get"}, {"detail": "\n@overload\ndef groupby(\n self : DataFrame,\n by : Scalar,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Scalar, Literal[True]]: ...\ndef groupby(\n self : DataFrame,\n by : Scalar,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Scalar, Literal[False]]: ...\ndef groupby(\n self : DataFrame,\n by : DatetimeIndex,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Timestamp, Literal[True]]: ...\ndef groupby(\n self : DataFrame,\n by : DatetimeIndex,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Timestamp, Literal[False]]: ...\ndef groupby(\n self : DataFrame,\n by : TimedeltaIndex,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Timedelta, Literal[True]]: ...\ndef groupby(\n self : DataFrame,\n by : TimedeltaIndex,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Timedelta, Literal[False]]: ...\ndef groupby(\n self : DataFrame,\n by : PeriodIndex,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Period, Literal[True]]: ...\ndef groupby(\n self : DataFrame,\n by : PeriodIndex,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Period, Literal[False]]: ...\ndef groupby[IntervalT: Interval[Unknown]](\n self : DataFrame,\n by : IntervalIndex[IntervalT],\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[IntervalT, Literal[True]]: ...\ndef groupby[IntervalT: Interval[Unknown]](\n self : DataFrame,\n by : IntervalIndex[IntervalT],\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[IntervalT, Literal[False]]: ...\ndef groupby(\n self : DataFrame,\n by : ((...) -> Any) | Grouper | Mapping[Hashable | None, Any] | MultiIndex | list[((...) -> Any) | ufunc] | list[Grouper] | list[Index] | list[Mapping[Hashable | None, Any]] | list[Series] | list[ndarray] | list[Unknown] | ndarray | ufunc | tuple[Unknown, ...] | None = ...,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[tuple[Unknown, ...], Literal[True]]: ...\ndef groupby(\n self : DataFrame,\n by : ((...) -> Any) | Grouper | Mapping[Hashable | None, Any] | MultiIndex | list[((...) -> Any) | ufunc] | list[Grouper] | list[Index] | list[Mapping[Hashable | None, Any]] | list[Series] | list[ndarray] | list[Unknown] | ndarray | ufunc | tuple[Unknown, ...] | None = ...,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[tuple[Unknown, ...], Literal[False]]: ...\ndef groupby[SeriesByT: Interval[Timedelta | Timestamp | float | int] | Period | bool | bytes | complex | date | datetime | float | int | str | timedelta](\n self : DataFrame,\n by : Series[SeriesByT],\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[SeriesByT, Literal[True]]: ...\ndef groupby[SeriesByT: Interval[Timedelta | Timestamp | float | int] | Period | bool | bytes | complex | date | datetime | float | int | str | timedelta](\n self : DataFrame,\n by : Series[SeriesByT],\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[SeriesByT, Literal[False]]: ...\ndef groupby(\n self : DataFrame,\n by : CategoricalIndex | Index | Series,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Any, Literal[True]]: ...\ndef groupby(\n self : DataFrame,\n by : CategoricalIndex | Index | Series,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Any, Literal[False]]: ...", "kind": 2, "label": "groupby", "sortText": "0z.9999.groupby"}, {"detail": "def gt(\n self : DataFrame,\n other: DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level: Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "gt", "sortText": "0z.9999.gt"}, {"detail": "def head(\n self: DataFrame,\n n : int = 5\n) -> DataFrame: ...", "kind": 2, "label": "head", "sortText": "0z.9999.head"}, {"detail": "def hist(\n self : DataFrame,\n by : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | str | None = None,\n bins : int | list[Unknown] = 10,\n *,\n grid : bool = True,\n xlabelsize: float | str | None = None,\n xrot : float | None = None,\n ylabelsize: float | str | None = None,\n yrot : float | None = None,\n ax : Unknown = None,\n sharex : bool = False,\n sharey : bool = False,\n figsize : tuple[float, float] | None = None,\n layout : tuple[int, int] | None = None,\n backend : str | None = None,\n legend : bool = False,\n **kwargs : Any\n) -> ndarray[tuple[Any, ...], dtype[object_]]: ...", "kind": 2, "label": "hist", "sortText": "0z.9999.hist"}, {"detail": "_iAtIndexerFrame", "kind": 5, "label": "iat", "sortText": "0z.9999.iat"}, {"detail": "def idxmax(\n self : DataFrame,\n axis : Axis = 0,\n skipna : bool = True,\n numeric_only: bool = False\n) -> Series[int]: ...", "kind": 2, "label": "idxmax", "sortText": "0z.9999.idxmax"}, {"detail": "def idxmin(\n self : DataFrame,\n axis : Axis = 0,\n skipna : bool = True,\n numeric_only: bool = False\n) -> Series[int]: ...", "kind": 2, "label": "idxmin", "sortText": "0z.9999.idxmin"}, {"detail": "_iLocIndexerFrame[DataFrame]", "kind": 5, "label": "iloc", "sortText": "0z.9999.iloc"}, {"detail": "Index", "kind": 5, "label": "index", "sortText": "0z.9999.index"}, {"detail": "def infer_objects(\n self: DataFrame,\n copy: bool | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "infer_objects", "sortText": "0z.9999.infer_objects"}, {"detail": "def info(\n self : DataFrame,\n verbose : bool | None = ...,\n buf : WriteBuffer[str] | None = ...,\n max_cols : int | None = ...,\n memory_usage: Literal['deep'] | bool | None = ...,\n show_counts : bool | None = ...\n) -> None: ...", "kind": 2, "label": "info", "sortText": "0z.9999.info"}, {"detail": "def insert(\n self : DataFrame,\n loc : int,\n column : Hashable,\n value : Index | Sequence[Unknown] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray[tuple[int]] | str | timedelta | timedelta64 | None,\n allow_duplicates: bool = ...\n) -> None: ...", "kind": 2, "label": "insert", "sortText": "0z.9999.insert"}, {"detail": "\n@overload\ndef interpolate(\n self : DataFrame,\n method : InterpolateOptions = ...,\n *,\n axis : Axis = 0,\n limit : int | None = ...,\n limit_direction: Literal['backward', 'both', 'forward'] = ...,\n limit_area : Literal['inside', 'outside'] | None = ...,\n inplace : Literal[True],\n **kwargs : Any\n) -> None: ...\ndef interpolate(\n self : DataFrame,\n method : InterpolateOptions = ...,\n *,\n axis : Axis = 0,\n limit : int | None = ...,\n limit_direction: Literal['backward', 'both', 'forward'] = ...,\n limit_area : Literal['inside', 'outside'] | None = ...,\n inplace : Literal[False] = False,\n **kwargs : Any\n) -> DataFrame: ...", "kind": 2, "label": "interpolate", "sortText": "0z.9999.interpolate"}, {"detail": "def isetitem(\n self: DataFrame,\n loc: Sequence[int] | int,\n value: ExtensionArray | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | list[Any] | ndarray | str | timedelta | timedelta64\n) -> None: ...", "kind": 2, "label": "isetitem", "sortText": "0z.9999.isetitem"}, {"detail": "def isin(\n self: DataFrame,\n values: DataFrame | Iterable[Any] | Mapping[Hashable, Iterable[Any]]\n) -> DataFrame: ...", "kind": 2, "label": "isin", "sortText": "0z.9999.isin"}, {"detail": "def isna(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "isna", "sortText": "0z.9999.isna"}, {"detail": "def isnull(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "isnull", "sortText": "0z.9999.isnull"}, {"detail": "def items(self: DataFrame) -> Iterator[tuple[Hashable, Series]]: ...", "kind": 2, "label": "items", "sortText": "0z.9999.items"}, {"detail": "def iterrows(self: DataFrame) -> Iterator[tuple[Hashable, Series]]: ...", "kind": 2, "label": "iterrows", "sortText": "0z.9999.iterrows"}, {"detail": "\n@overload\ndef itertuples(\n self : DataFrame,\n index: bool = ...,\n name : str = ...\n) -> Iterator[_PandasNamedTuple]: ...\ndef itertuples(\n self : DataFrame,\n index: bool = ...,\n name : None = None\n) -> Iterator[tuple[Any, ...]]: ...", "kind": 2, "label": "itertuples", "sortText": "0z.9999.itertuples"}, {"detail": "def join(\n self : DataFrame,\n other : DataFrame | Series | list[DataFrame | Series],\n on : list[str] | str | None = ...,\n how : MergeHow = 'left',\n lsuffix : str = '',\n rsuffix : str = '',\n sort : bool = False,\n validate: Literal['1:1', '1:m', 'm:1', 'm:m', 'many_to_many', 'many_to_one', 'one_to_many', 'one_to_one'] | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "join", "sortText": "0z.9999.join"}, {"detail": "def keys(self: DataFrame) -> Index: ...", "kind": 2, "label": "keys", "sortText": "0z.9999.keys"}, {"detail": "def kurt(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "kurt", "sortText": "0z.9999.kurt"}, {"detail": "def kurtosis(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "kurtosis", "sortText": "0z.9999.kurtosis"}, {"detail": "def last_valid_index(self: DataFrame) -> Scalar: ...", "kind": 2, "label": "last_valid_index", "sortText": "0z.9999.last_valid_index"}, {"detail": "def le(\n self : DataFrame,\n other: DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level: Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "le", "sortText": "0z.9999.le"}, {"detail": "_LocIndexerFrame[DataFrame]", "kind": 5, "label": "loc", "sortText": "0z.9999.loc"}, {"detail": "def lt(\n self : DataFrame,\n other: DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level: Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "lt", "sortText": "0z.9999.lt"}, {"detail": "def map(\n self : DataFrame,\n func : (...) -> Unknown,\n na_action: Literal['ignore'] | None = None,\n **kwargs : Any\n) -> DataFrame: ...", "kind": 2, "label": "map", "sortText": "0z.9999.map"}, {"detail": "\n@overload\ndef mask(\n self : DataFrame,\n cond : ((DataFrame) -> DataFrame) | ((Any) -> builtins.bool) | DataFrame | Series | ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other : ((...) -> Unknown) | DataFrame | NAType | Series | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n *,\n inplace: Literal[True],\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n level : Hashable | None = None\n) -> None: ...\ndef mask(\n self : DataFrame,\n cond : ((DataFrame) -> DataFrame) | ((Any) -> builtins.bool) | DataFrame | Series | ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other : ((...) -> Unknown) | DataFrame | NAType | Series | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n *,\n inplace: Literal[False] = False,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n level : Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "mask", "sortText": "0z.9999.mask"}, {"detail": "\n@overload\ndef max(\n self : DataFrame,\n axis : None,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Scalar: ...\ndef max(\n self : DataFrame,\n axis : Axis = 0,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "max", "sortText": "0z.9999.max"}, {"detail": "\n@overload\ndef mean(\n self : DataFrame,\n axis : None,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Scalar: ...\ndef mean(\n self : DataFrame,\n axis : Axis = 0,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "mean", "sortText": "0z.9999.mean"}, {"detail": "\n@overload\ndef median(\n self : DataFrame,\n axis : None,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Scalar: ...\ndef median(\n self : DataFrame,\n axis : Axis = 0,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "median", "sortText": "0z.9999.median"}, {"detail": "def melt(\n self : DataFrame,\n id_vars : Sequence[Unknown] | ndarray | tuple[Unknown, ...] | None = ...,\n value_vars : Sequence[Unknown] | ndarray | tuple[Unknown, ...] | None = ...,\n var_name : Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = None,\n value_name : Scalar = 'value',\n col_level : int | str | None = ...,\n ignore_index: bool = True\n) -> DataFrame: ...", "kind": 2, "label": "melt", "sortText": "0z.9999.melt"}, {"detail": "def memory_usage(\n self : DataFrame,\n index: bool = True,\n deep : bool = False\n) -> Series: ...", "kind": 2, "label": "memory_usage", "sortText": "0z.9999.memory_usage"}, {"detail": "def merge(\n self : DataFrame,\n right : DataFrame | Series,\n how : MergeHow = 'inner',\n on : ExtensionArray | Hashable | Index | Sequence[Hashable] | Series | ndarray | None = ...,\n left_on : ExtensionArray | Hashable | Index | Sequence[Hashable] | Series | ndarray | None = ...,\n right_on : ExtensionArray | Hashable | Index | Sequence[Hashable] | Series | ndarray | None = ...,\n left_index : bool = False,\n right_index: bool = False,\n sort : bool = False,\n suffixes : Suffixes = ...,\n copy : bool = True,\n indicator : bool | str = False,\n validate : Literal['1:1', '1:m', 'm:1', 'm:m', 'many_to_many', 'many_to_one', 'one_to_many', 'one_to_one'] | None = None\n) -> DataFrame: ...", "kind": 2, "label": "merge", "sortText": "0z.9999.merge"}, {"detail": "\n@overload\ndef min(\n self : DataFrame,\n axis : None,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Scalar: ...\ndef min(\n self : DataFrame,\n axis : Axis = 0,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "min", "sortText": "0z.9999.min"}, {"detail": "def mod(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | float | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "mod", "sortText": "0z.9999.mod"}, {"detail": "def mode(\n self : DataFrame,\n axis : Axis = 0,\n numeric_only: bool = False,\n dropna : bool = True\n) -> Series: ...", "kind": 2, "label": "mode", "sortText": "0z.9999.mode"}, {"detail": "def mul(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "mul", "sortText": "0z.9999.mul"}, {"detail": "def mul(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "multiply", "sortText": "0z.9999.multiply"}, {"detail": "int", "kind": 5, "label": "ndim", "sortText": "0z.9999.ndim"}, {"detail": "def ne(\n self : DataFrame,\n other: DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level: Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "ne", "sortText": "0z.9999.ne"}, {"detail": "def nlargest(\n self : DataFrame,\n n : int,\n columns: list[str] | str,\n keep : NsmallestNlargestKeep = 'first'\n) -> DataFrame: ...", "kind": 2, "label": "nlargest", "sortText": "0z.9999.nlargest"}, {"detail": "def notna(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "notna", "sortText": "0z.9999.notna"}, {"detail": "def notnull(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "notnull", "sortText": "0z.9999.notnull"}, {"detail": "def nsmallest(\n self : DataFrame,\n n : int,\n columns: list[str] | str,\n keep : NsmallestNlargestKeep = 'first'\n) -> DataFrame: ...", "kind": 2, "label": "nsmallest", "sortText": "0z.9999.nsmallest"}, {"detail": "def nunique(\n self : DataFrame,\n axis : Axis = 0,\n dropna: bool = True\n) -> Series[int]: ...", "kind": 2, "label": "nunique", "sortText": "0z.9999.nunique"}, {"detail": "def pct_change(\n self : DataFrame,\n periods : int = 1,\n fill_method: None = None,\n freq : BaseOffset | str | timedelta | None = ...,\n fill_value : NAType | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "pct_change", "sortText": "0z.9999.pct_change"}, {"detail": "\n@overload\ndef pipe[**P, T](\n DataFrame,\n (\n DataFrame,\n ParamSpec(P)\n ) -> T,\n ParamSpec(P)\n) -> T: ...\ndef pipe[T](\n self: DataFrame,\n func: tuple[(...) -> T, str],\n *args: Any,\n **kwargs: Any\n) -> T: ...", "kind": 2, "label": "pipe", "sortText": "0z.9999.pipe"}, {"detail": "def pivot(\n self : DataFrame,\n *,\n columns: IndexLabel,\n index : IndexLabel = ...,\n values : IndexLabel = ...\n) -> DataFrame: ...", "kind": 2, "label": "pivot", "sortText": "0z.9999.pivot"}, {"detail": "def pivot_table(\n self : DataFrame,\n values : _PivotTableValuesTypes = None,\n index : _PivotTableIndexTypes = None,\n columns : _PivotTableColumnsTypes = None,\n aggfunc : Literal['count', 'max', 'mean', 'median', 'min', 'std', 'sum', 'var'] | ((Series) -> Unknown) | Mapping[Hashable, _PivotAggFunc] | Sequence[_PivotAggFunc] | ufunc = 'mean',\n fill_value : Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = None,\n margins : bool = False,\n dropna : bool = True,\n margins_name: str = 'All',\n observed : bool = True,\n sort : bool = True\n) -> DataFrame: ...", "kind": 2, "label": "pivot_table", "sortText": "0z.9999.pivot_table"}, {"detail": "PlotAccessor", "kind": 5, "label": "plot", "sortText": "0z.9999.plot"}, {"detail": "def pop(\n self: DataFrame,\n item: str\n) -> Series: ...", "kind": 2, "label": "pop", "sortText": "0z.9999.pop"}, {"detail": "def pow(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "pow", "sortText": "0z.9999.pow"}, {"detail": "def prod(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n skipna : bool | None = True,\n numeric_only: bool = False,\n min_count : int = 0,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "prod", "sortText": "0z.9999.prod"}, {"detail": "def prod(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n skipna : bool | None = True,\n numeric_only: bool = False,\n min_count : int = 0,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "product", "sortText": "0z.9999.product"}, {"detail": "\n@overload\ndef quantile(\n self : DataFrame,\n q : float = ...,\n axis : Axis = 0,\n numeric_only : bool = ...,\n interpolation: QuantileInterpolation = ...,\n method : CalculationMethod = ...\n) -> Series: ...\ndef quantile(\n self : DataFrame,\n q : Sequence[float] | ndarray[tuple[Any, ...], dtype[floating]],\n axis : Axis = 0,\n numeric_only : bool = ...,\n interpolation: QuantileInterpolation = ...,\n method : CalculationMethod = ...\n) -> DataFrame: ...", "kind": 2, "label": "quantile", "sortText": "0z.9999.quantile"}, {"detail": "\n@overload\ndef query(\n self : DataFrame,\n expr : str,\n *,\n parser : Literal['pandas', 'python'] = ...,\n engine : Literal['numexpr', 'python'] | None = ...,\n local_dict : dict[str, Any] | None = ...,\n global_dict: dict[str, Any] | None = ...,\n resolvers : list[Mapping[Unknown, Unknown]] | None = ...,\n level : int = ...,\n target : object | None = ...,\n inplace : Literal[True]\n) -> None: ...\ndef query(\n self : DataFrame,\n expr : str,\n *,\n inplace : Literal[False] = False,\n parser : Literal['pandas', 'python'] = ...,\n engine : Literal['numexpr', 'python'] | None = ...,\n local_dict : dict[str, Any] | None = ...,\n global_dict: dict[str, Any] | None = ...,\n resolvers : list[Mapping[Unknown, Unknown]] | None = ...,\n level : int = ...,\n target : object | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "query", "sortText": "0z.9999.query"}, {"detail": "def radd(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "radd", "sortText": "0z.9999.radd"}, {"detail": "def rank(\n self : DataFrame,\n axis : Axis = 0,\n method : Literal['average', 'dense', 'first', 'max', 'min'] = 'average',\n numeric_only: bool = False,\n na_option : Literal['bottom', 'keep', 'top'] = 'keep',\n ascending : bool = True,\n pct : bool = False\n) -> DataFrame: ...", "kind": 2, "label": "rank", "sortText": "0z.9999.rank"}, {"detail": "def rtruediv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rdiv", "sortText": "0z.9999.rdiv"}, {"detail": "def reindex(\n self : DataFrame,\n labels : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = ...,\n *,\n index : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = ...,\n columns : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n method : Literal['backfill', 'bfill', 'ffill', 'nearest', 'pad'] | None = ...,\n copy : bool = True,\n level : int | str = ...,\n fill_value: Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n limit : int | None = None,\n tolerance : Timedelta | float | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "reindex", "sortText": "0z.9999.reindex"}, {"detail": "def reindex_like(\n self : DataFrame,\n other : DataFrame,\n method : Literal['backfill', 'bfill', 'ffill', 'nearest', 'pad'] | None = ...,\n copy : bool = True,\n limit : int | None = None,\n tolerance: ExtensionArray | Index | Sequence[Scalar] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64 = ...\n) -> DataFrame: ...", "kind": 2, "label": "reindex_like", "sortText": "0z.9999.reindex_like"}, {"detail": "\n@overload\ndef rename(\n self : DataFrame,\n mapper : ((Any) -> Hashable | None) | Mapping[Any, Hashable | None] | None = ...,\n *,\n index : ((Any) -> Hashable | None) | Mapping[Any, Hashable | None] | None = ...,\n columns: ((Any) -> Hashable | None) | Mapping[Any, Hashable | None] | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n copy : bool = ...,\n inplace: Literal[True],\n level : Hashable | None = None,\n errors : IgnoreRaise = ...\n) -> None: ...\ndef rename(\n self : DataFrame,\n mapper : ((Any) -> Hashable | None) | Mapping[Any, Hashable | None] | None = ...,\n *,\n index : ((Any) -> Hashable | None) | Mapping[Any, Hashable | None] | None = ...,\n columns: ((Any) -> Hashable | None) | Mapping[Any, Hashable | None] | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n copy : bool = ...,\n inplace: Literal[False] = False,\n level : Hashable | None = None,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...", "kind": 2, "label": "rename", "sortText": "0z.9999.rename"}, {"detail": "\n@overload\ndef rename_axis(\n self : DataFrame,\n mapper : ExtensionArray | Index | SequenceNotStr[Any] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | range | str | timedelta | timedelta64 | None = ...,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n copy : bool = ...,\n inplace: Literal[True]\n) -> None: ...\ndef rename_axis(\n self : DataFrame,\n mapper : ExtensionArray | Index | SequenceNotStr[Any] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | range | str | timedelta | timedelta64 | None = ...,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n copy : bool = ...,\n inplace: Literal[False] = False\n) -> DataFrame: ...\ndef rename_axis(\n self : DataFrame,\n *,\n index : ((...) -> Unknown) | Sequence[str] | dict[int | str, str] | str | None = ...,\n columns: ((...) -> Unknown) | Sequence[str] | dict[int | str, str] | str | None = ...,\n copy : bool = ...,\n inplace: Literal[True]\n) -> None: ...\ndef rename_axis(\n self : DataFrame,\n *,\n index : ((...) -> Unknown) | Sequence[str] | dict[int | str, str] | str | None = ...,\n columns: ((...) -> Unknown) | Sequence[str] | dict[int | str, str] | str | None = ...,\n copy : bool = ...,\n inplace: Literal[False] = False\n) -> DataFrame: ...", "kind": 2, "label": "rename_axis", "sortText": "0z.9999.rename_axis"}, {"detail": "def reorder_levels(\n self : DataFrame,\n order: list[Unknown],\n axis : Axis = 0\n) -> DataFrame: ...", "kind": 2, "label": "reorder_levels", "sortText": "0z.9999.reorder_levels"}, {"detail": "\n@overload\ndef replace[HashableT2: Hashable, HashableT3: Hashable](\n self : DataFrame,\n to_replace: Mapping[HashableT2, ReplaceValue] | Mapping[Unknown, Unknown] | NAType | Pattern[Unknown] | Sequence[Pattern[Unknown] | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n value : Mapping[HashableT3, ReplaceValue] | Mapping[Unknown, Unknown] | NAType | Pattern[Unknown] | Sequence[Pattern[Unknown] | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n *,\n inplace : Literal[True],\n regex : Mapping[HashableT3, ReplaceValue] | Mapping[Unknown, Unknown] | NAType | Pattern[Unknown] | Sequence[Pattern[Unknown] | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...\n) -> None: ...\ndef replace[HashableT2: Hashable, HashableT3: Hashable](\n self : DataFrame,\n to_replace: Mapping[HashableT2, ReplaceValue] | Mapping[Unknown, Unknown] | NAType | Pattern[Unknown] | Sequence[Pattern[Unknown] | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n value : Mapping[HashableT3, ReplaceValue] | Mapping[Unknown, Unknown] | NAType | Pattern[Unknown] | Sequence[Pattern[Unknown] | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n *,\n inplace : Literal[False] = False,\n regex : Mapping[HashableT3, ReplaceValue] | Mapping[Unknown, Unknown] | NAType | Pattern[Unknown] | Sequence[Pattern[Unknown] | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "replace", "sortText": "0z.9999.replace"}, {"detail": "def resample(\n self : DataFrame,\n rule : BaseOffset | str | timedelta,\n axis : Literal['columns', 'index', 0, 1] | _NoDefault = 0,\n closed : Literal['left', 'right'] | None = None,\n label : Literal['left', 'right'] | None = None,\n on : Hashable | None = None,\n level : Hashable | None = None,\n origin : Timestamp | date | datetime | datetime64 | float | integer | str = 'start_day',\n offset : Timedelta | float | integer | str | timedelta | timedelta64 | None = None,\n group_keys: bool = False\n) -> DatetimeIndexResampler[DataFrame]: ...", "kind": 2, "label": "resample", "sortText": "0z.9999.resample"}, {"detail": "\n@overload\ndef reset_index(\n self : DataFrame,\n level : Hashable | Sequence[Hashable] = ...,\n *,\n drop : bool = ...,\n col_level : int | str = ...,\n col_fill : Hashable = ...,\n inplace : Literal[True],\n allow_duplicates: bool = ...,\n names : Hashable | Sequence[Hashable] = ...\n) -> None: ...\ndef reset_index(\n self : DataFrame,\n level : Hashable | Sequence[Hashable] = ...,\n *,\n col_level : int | str = ...,\n col_fill : Hashable = ...,\n drop : bool = ...,\n inplace : Literal[False] = False,\n allow_duplicates: bool = ...,\n names : Hashable | Sequence[Hashable] = ...\n) -> DataFrame: ...", "kind": 2, "label": "reset_index", "sortText": "0z.9999.reset_index"}, {"detail": "def rfloordiv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rfloordiv", "sortText": "0z.9999.rfloordiv"}, {"detail": "def rmod(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rmod", "sortText": "0z.9999.rmod"}, {"detail": "def rmul(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rmul", "sortText": "0z.9999.rmul"}, {"detail": "\n@overload\ndef rolling(\n self : DataFrame,\n window : BaseIndexer | BaseOffset | int | str | timedelta,\n min_periods: int | None = ...,\n center : bool = ...,\n on : Hashable | None = ...,\n axis : AxisIndex = ...,\n closed : Literal['both', 'left', 'neither', 'right'] | None = ...,\n step : int | None = ...,\n method : CalculationMethod = ...,\n *,\n win_type : str\n) -> Window[DataFrame]: ...\ndef rolling(\n self : DataFrame,\n window : BaseIndexer | BaseOffset | int | str | timedelta,\n min_periods: int | None = ...,\n center : bool = ...,\n on : Hashable | None = ...,\n axis : AxisIndex = ...,\n closed : Literal['both', 'left', 'neither', 'right'] | None = ...,\n step : int | None = ...,\n method : CalculationMethod = ...,\n *,\n win_type : None = None\n) -> Rolling[DataFrame]: ...", "kind": 2, "label": "rolling", "sortText": "0z.9999.rolling"}, {"detail": "def round(\n self : DataFrame,\n decimals: Series | dict[Unknown, Unknown] | int = ...,\n *args : Any,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "round", "sortText": "0z.9999.round"}, {"detail": "def rpow(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rpow", "sortText": "0z.9999.rpow"}, {"detail": "def rsub(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rsub", "sortText": "0z.9999.rsub"}, {"detail": "def rtruediv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rtruediv", "sortText": "0z.9999.rtruediv"}, {"detail": "def sample(\n self : DataFrame,\n n : int | None = ...,\n frac : float | None = ...,\n replace : bool = False,\n weights : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | str | None = ...,\n random_state: BitGenerator | ExtensionArray | Generator | RandomState | int | ndarray | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n ignore_index: bool = False\n) -> DataFrame: ...", "kind": 2, "label": "sample", "sortText": "0z.9999.sample"}, {"detail": "\n@overload\ndef select_dtypes(\n self : DataFrame,\n include: StrDtypeArg,\n exclude: Literal[' Never: ...\ndef select_dtypes(\n self: DataFrame,\n include: Literal[' Never: ...\ndef select_dtypes(\n self: DataFrame,\n exclude: StrDtypeArg\n) -> Never: ...\ndef select_dtypes(\n self: DataFrame,\n include: list[Never],\n exclude: list[Never]\n) -> Never: ...\ndef select_dtypes(\n self : DataFrame,\n include: _AstypeArgExtList,\n exclude: Literal[' DataFrame: ...\ndef select_dtypes(\n self: DataFrame,\n include: Literal[' DataFrame: ...\ndef select_dtypes(\n self: DataFrame,\n exclude: _AstypeArgExtList\n) -> DataFrame: ...", "kind": 2, "label": "select_dtypes", "sortText": "0z.9999.select_dtypes"}, {"detail": "def sem(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n skipna : bool | None = True,\n ddof : int = 1,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "sem", "sortText": "0z.9999.sem"}, {"detail": "def set_axis(\n self : DataFrame,\n labels: AxesData,\n *,\n axis : Axis = 0\n) -> DataFrame: ...", "kind": 2, "label": "set_axis", "sortText": "0z.9999.set_axis"}, {"detail": "def set_flags(\n self : DataFrame,\n *,\n copy : bool = ...,\n allows_duplicate_labels: bool | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "set_flags", "sortText": "0z.9999.set_flags"}, {"detail": "\n@overload\ndef set_index(\n self : DataFrame,\n keys : Hashable | Index | Iterator[Hashable] | Sequence[Hashable] | Series | ndarray | None,\n *,\n drop : bool = ...,\n append : bool = ...,\n verify_integrity: bool = ...,\n inplace : Literal[True]\n) -> None: ...\ndef set_index(\n self : DataFrame,\n keys : Hashable | Index | Iterator[Hashable] | Sequence[Hashable] | Series | ndarray | None,\n *,\n drop : bool = ...,\n append : bool = ...,\n verify_integrity: bool = ...,\n inplace : Literal[False] = False\n) -> DataFrame: ...", "kind": 2, "label": "set_index", "sortText": "0z.9999.set_index"}, {"detail": "tuple[int, int]", "kind": 5, "label": "shape", "sortText": "0z.9999.shape"}, {"detail": "def shift(\n self : DataFrame,\n periods : Sequence[int] | int = ...,\n freq : BaseOffset | str | timedelta | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n fill_value: NAType | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "shift", "sortText": "0z.9999.shift"}, {"detail": "int", "kind": 5, "label": "size", "sortText": "0z.9999.size"}, {"detail": "def skew(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "skew", "sortText": "0z.9999.skew"}, {"detail": "\n@overload\ndef sort_index(\n self : DataFrame,\n *,\n axis : Axis = 0,\n level : Hashable | None = None,\n ascending : Sequence[bool] | bool = ...,\n kind : SortKind = ...,\n na_position : NaPosition = ...,\n sort_remaining: bool = ...,\n ignore_index : bool = ...,\n inplace : Literal[True],\n key : IndexKeyFunc = ...\n) -> None: ...\ndef sort_index(\n self : DataFrame,\n *,\n axis : Axis = 0,\n level : Hashable | list[int] | list[str] | None = ...,\n ascending : Sequence[bool] | bool = ...,\n kind : SortKind = ...,\n na_position : NaPosition = ...,\n sort_remaining: bool = ...,\n ignore_index : bool = ...,\n inplace : Literal[False] = False,\n key : IndexKeyFunc = ...\n) -> DataFrame: ...", "kind": 2, "label": "sort_index", "sortText": "0z.9999.sort_index"}, {"detail": "\n@overload\ndef sort_values(\n self : DataFrame,\n by : Sequence[str] | str,\n *,\n axis : Axis = 0,\n ascending : Sequence[bool] | bool = ...,\n kind : SortKind = ...,\n na_position : NaPosition = ...,\n ignore_index: bool = ...,\n inplace : Literal[True],\n key : ValueKeyFunc = ...\n) -> None: ...\ndef sort_values(\n self : DataFrame,\n by : Sequence[str] | str,\n *,\n axis : Axis = 0,\n ascending : Sequence[bool] | bool = ...,\n kind : SortKind = ...,\n na_position : NaPosition = ...,\n ignore_index: bool = ...,\n inplace : Literal[False] = False,\n key : ValueKeyFunc = ...\n) -> DataFrame: ...", "kind": 2, "label": "sort_values", "sortText": "0z.9999.sort_values"}, {"detail": "EllipsisType", "kind": 5, "label": "sparse", "sortText": "0z.9999.sparse"}, {"detail": "def squeeze(\n self: DataFrame,\n axis: Literal['columns', 'index', 0, 1] | None = None\n) -> DataFrame | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64: ...", "kind": 2, "label": "squeeze", "sortText": "0z.9999.squeeze"}, {"detail": "\n@overload\ndef stack(\n self : DataFrame,\n level : IndexLabel = ...,\n *,\n future_stack: Literal[True]\n) -> DataFrame | Series: ...\ndef stack(\n self : DataFrame,\n level : IndexLabel = ...,\n dropna : bool = ...,\n sort : bool = ...,\n future_stack: Literal[False] = False\n) -> DataFrame | Series: ...", "kind": 2, "label": "stack", "sortText": "0z.9999.stack"}, {"detail": "def std(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = 0,\n skipna : bool = True,\n ddof : int = 1,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "std", "sortText": "0z.9999.std"}, {"detail": "Styler", "kind": 5, "label": "style", "sortText": "0z.9999.style"}, {"detail": "def sub(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "sub", "sortText": "0z.9999.sub"}, {"detail": "def sub(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "subtract", "sortText": "0z.9999.subtract"}, {"detail": "def sum(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = 0,\n skipna : bool | None = True,\n numeric_only: bool = False,\n min_count : int = 0,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "sum", "sortText": "0z.9999.sum"}, {"detail": "def swapaxes(\n self : DataFrame,\n axis1: Axis,\n axis2: Axis,\n copy : bool = ...\n) -> DataFrame: ...", "kind": 2, "label": "swapaxes", "sortText": "0z.9999.swapaxes"}, {"detail": "def swaplevel(\n self: DataFrame,\n i : Hashable = ...,\n j : Hashable = ...,\n axis: Axis = 0\n) -> DataFrame: ...", "kind": 2, "label": "swaplevel", "sortText": "0z.9999.swaplevel"}, {"detail": "def tail(\n self: DataFrame,\n n : int = 5\n) -> DataFrame: ...", "kind": 2, "label": "tail", "sortText": "0z.9999.tail"}, {"detail": "def take(\n self : DataFrame,\n indices : TakeIndexer,\n axis : Axis = 0,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "take", "sortText": "0z.9999.take"}, {"detail": "def to_clipboard[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n excel : bool = True,\n sep : str | None = None,\n *,\n na_rep : str = ...,\n float_format : ((object) -> str) | str | None = ...,\n columns : list[HashableT1] | None = ...,\n header : bool | list[str] = ...,\n index : bool = ...,\n index_label : Literal[False] | list[HashableT2] | str | None = ...,\n mode : FileWriteMode = ...,\n encoding : str | None = ...,\n compression : CompressionOptions = ...,\n quoting : CSVQuoting = ...,\n quotechar : str = ...,\n lineterminator : str | None = ...,\n chunksize : int | None = ...,\n date_format : str | None = ...,\n doublequote : bool = ...,\n escapechar : str | None = ...,\n decimal : str = ...,\n errors : str = ...,\n storage_options: StorageOptions = ...\n) -> None: ...", "kind": 2, "label": "to_clipboard", "sortText": "0z.9999.to_clipboard"}, {"detail": "\n@overload\ndef to_csv[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n path_or_buf : PathLike[str] | WriteBuffer[bytes] | WriteBuffer[str] | str,\n sep : str = ...,\n na_rep : str = ...,\n float_format : ((object) -> str) | str | None = ...,\n columns : list[HashableT1] | None = ...,\n header : bool | list[str] = ...,\n index : bool = ...,\n index_label : Literal[False] | list[HashableT2] | str | None = ...,\n mode : FileWriteMode = ...,\n encoding : str | None = ...,\n compression : CompressionOptions = ...,\n quoting : CSVQuoting = ...,\n quotechar : str = ...,\n lineterminator : str | None = ...,\n chunksize : int | None = ...,\n date_format : str | None = ...,\n doublequote : bool = ...,\n escapechar : str | None = ...,\n decimal : str = ...,\n errors : OpenFileErrors = ...,\n storage_options: StorageOptions = ...\n) -> None: ...\ndef to_csv[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n path_or_buf : None = None,\n sep : str = ...,\n na_rep : str = ...,\n float_format : ((object) -> str) | str | None = ...,\n columns : list[HashableT1] | None = ...,\n header : bool | list[str] = ...,\n index : bool = ...,\n index_label : Literal[False] | list[HashableT2] | str | None = ...,\n mode : FileWriteMode = ...,\n encoding : str | None = ...,\n compression : CompressionOptions = ...,\n quoting : CSVQuoting = ...,\n quotechar : str = ...,\n lineterminator : str | None = ...,\n chunksize : int | None = ...,\n date_format : str | None = ...,\n doublequote : bool = ...,\n escapechar : str | None = ...,\n decimal : str = ...,\n errors : OpenFileErrors = ...,\n storage_options: StorageOptions = ...\n) -> str: ...", "kind": 2, "label": "to_csv", "sortText": "0z.9999.to_csv"}, {"detail": "\n@overload\ndef to_dict(\n self : DataFrame,\n orient: str = ...,\n *,\n into : type[defaultdict[Unknown, Unknown]],\n index : Literal[True] = True\n) -> Never: ...\ndef to_dict[_T_MUTABLE_MAPPING_co: MutableMapping[Unknown, Unknown]](\n self : DataFrame,\n orient: Literal['records'],\n *,\n into : _T_MUTABLE_MAPPING_co | type[_T_MUTABLE_MAPPING_co],\n index : Literal[True] = True\n) -> list[_T_MUTABLE_MAPPING_co]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['records'],\n *,\n into : type[dict[Unknown, Unknown]] = ...,\n index : Literal[True] = True\n) -> list[dict[Hashable, Any]]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['index'],\n *,\n into : defaultdict[Unknown, Unknown],\n index : Literal[True] = True\n) -> defaultdict[Hashable, dict[Hashable, Any]]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['index'],\n *,\n into : OrderedDict[Unknown, Unknown] | type[OrderedDict[Unknown, Unknown]],\n index : Literal[True] = True\n) -> OrderedDict[Hashable, dict[Hashable, Any]]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['index'],\n *,\n into : type[MutableMapping[Unknown, Unknown]],\n index : Literal[True] = True\n) -> MutableMapping[Hashable, dict[Hashable, Any]]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['index'],\n *,\n into : type[dict[Unknown, Unknown]] = ...,\n index : Literal[True] = True\n) -> dict[Hashable, dict[Hashable, Any]]: ...\ndef to_dict[_T_MUTABLE_MAPPING_co: MutableMapping[Unknown, Unknown]](\n self : DataFrame,\n orient: Literal['dict', 'list', 'series'] = ...,\n *,\n into : _T_MUTABLE_MAPPING_co | type[_T_MUTABLE_MAPPING_co],\n index : Literal[True] = True\n) -> _T_MUTABLE_MAPPING_co: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['dict', 'list', 'series'] = ...,\n *,\n into : type[dict[Unknown, Unknown]] = ...,\n index : Literal[True] = True\n) -> dict[Hashable, Any]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['split', 'tight'],\n *,\n into : MutableMapping[Unknown, Unknown] | type[MutableMapping[Unknown, Unknown]],\n index : bool = ...\n) -> MutableMapping[str, list[Unknown]]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['split', 'tight'],\n *,\n into : type[dict[Unknown, Unknown]] = ...,\n index : bool = ...\n) -> dict[str, list[Unknown]]: ...", "kind": 2, "label": "to_dict", "sortText": "0z.9999.to_dict"}, {"detail": "def to_excel(\n self : DataFrame,\n excel_writer: Unknown,\n sheet_name : str = 'Sheet1',\n na_rep : str = '',\n float_format: str | None = ...,\n columns : Sequence[str] | str | None = ...,\n header : bool | list[str] = True,\n index : bool = True,\n index_label : Sequence[str] | str | None = ...,\n startrow : int = 0,\n startcol : int = 0,\n engine : str | None = ...,\n merge_cells : ExcelWriterMergeCells = True,\n inf_rep : str = 'inf',\n freeze_panes: tuple[int, int] | None = ...\n) -> None: ...", "kind": 2, "label": "to_excel", "sortText": "0z.9999.to_excel"}, {"detail": "def to_feather(\n self: DataFrame,\n path: PathLike[str] | WriteBuffer[bytes] | str,\n **kwargs: Any\n) -> None: ...", "kind": 2, "label": "to_feather", "sortText": "0z.9999.to_feather"}, {"detail": "def to_hdf[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n path_or_buf : HDFStore | PathLike[str] | str,\n *,\n key : str,\n mode : Literal['a', 'r+', 'w'] = ...,\n complevel : int | None = ...,\n complib : Literal['blosc', 'bzip2', 'lzo', 'zlib'] | None = ...,\n append : bool = ...,\n format : Literal['f', 'fixed', 't', 'table'] | None = ...,\n index : bool = ...,\n min_itemsize: dict[HashableT1, int] | int | None = ...,\n nan_rep : str | None = ...,\n dropna : bool | None = ...,\n data_columns: Literal[True] | list[HashableT2] | None = ...,\n errors : OpenFileErrors = ...,\n encoding : str = ...\n) -> None: ...", "kind": 2, "label": "to_hdf", "sortText": "0z.9999.to_hdf"}, {"detail": "\n@overload\ndef to_html(\n self : DataFrame,\n buf : PathLike[str] | WriteBuffer[str] | str,\n *,\n columns : Index | SequenceNotStr[Hashable] | Series | None = ...,\n col_space : Mapping[Hashable, int | str] | Sequence[int | str] | int | str | None = ...,\n header : bool = ...,\n index : bool = ...,\n na_rep : str = ...,\n formatters : Mapping[Hashable, (object) -> str] | list[(object) -> str] | tuple[(object) -> str, ...] | None = ...,\n float_format : ((float) -> str) | None = ...,\n sparsify : bool | None = ...,\n index_names : bool = ...,\n justify : Literal['center', 'end', 'inherit', 'initial', 'justify', 'justify-all', 'left', 'match-parent', 'right', 'start', 'unset'] | None = ...,\n max_rows : int | None = ...,\n max_cols : int | None = ...,\n show_dimensions: bool = ...,\n decimal : str = ...,\n bold_rows : bool = ...,\n classes : Sequence[str] | None = ...,\n escape : bool = ...,\n notebook : bool = ...,\n border : int | None = ...,\n table_id : str | None = ...,\n render_links : bool = ...,\n encoding : str | None = ...\n) -> None: ...\ndef to_html(\n self : DataFrame,\n buf : None = None,\n *,\n columns : Sequence[Hashable] | None = ...,\n col_space : Mapping[Hashable, int | str] | Sequence[int | str] | int | str | None = ...,\n header : bool = ...,\n index : bool = ...,\n na_rep : str = ...,\n formatters : Mapping[Hashable, (object) -> str] | list[(object) -> str] | tuple[(object) -> str, ...] | None = ...,\n float_format : ((float) -> str) | None = ...,\n sparsify : bool | None = ...,\n index_names : bool = ...,\n justify : Literal['center', 'end', 'inherit', 'initial', 'justify', 'justify-all', 'left', 'match-parent', 'right', 'start', 'unset'] | None = ...,\n max_rows : int | None = ...,\n max_cols : int | None = ...,\n show_dimensions: bool = ...,\n decimal : str = ...,\n bold_rows : bool = ...,\n classes : Sequence[str] | None = ...,\n escape : bool = ...,\n notebook : bool = ...,\n border : int | None = ...,\n table_id : str | None = ...,\n render_links : bool = ...,\n encoding : str | None = ...\n) -> str: ...", "kind": 2, "label": "to_html", "sortText": "0z.9999.to_html"}, {"detail": "\n@overload\ndef to_json(\n self : DataFrame,\n path_or_buf : PathLike[str] | WriteBuffer[str] | str,\n *,\n orient : Literal['records'],\n date_format : Literal['epoch', 'iso'] | None = ...,\n double_precision: int = ...,\n force_ascii : bool = ...,\n date_unit : TimeUnit = ...,\n default_handler : ((Any) -> JSONSerializable) | None = ...,\n lines : Literal[True],\n compression : CompressionOptions = ...,\n index : bool | None = ...,\n indent : int | None = ...,\n storage_options : dict[Unknown, Unknown] | None = ...,\n mode : Literal['a']\n) -> None: ...\ndef to_json(\n self : DataFrame,\n path_or_buf : None = None,\n *,\n orient : Literal['records'],\n date_format : Literal['epoch', 'iso'] | None = ...,\n double_precision: int = ...,\n force_ascii : bool = ...,\n date_unit : TimeUnit = ...,\n default_handler : ((Any) -> JSONSerializable) | None = ...,\n lines : Literal[True],\n compression : CompressionOptions = ...,\n index : bool | None = ...,\n indent : int | None = ...,\n storage_options : dict[Unknown, Unknown] | None = ...,\n mode : Literal['a']\n) -> str: ...\ndef to_json(\n self : DataFrame,\n path_or_buf : None = None,\n *,\n orient : Literal['columns', 'index', 'records', 'split', 'table', 'values'] | None = ...,\n date_format : Literal['epoch', 'iso'] | None = ...,\n double_precision: int = ...,\n force_ascii : bool = ...,\n date_unit : TimeUnit = ...,\n default_handler : ((Any) -> JSONSerializable) | None = ...,\n lines : bool = ...,\n compression : CompressionOptions = ...,\n index : bool | None = ...,\n indent : int | None = ...,\n storage_options : dict[Unknown, Unknown] | None = ...,\n mode : Literal['w'] = ...\n) -> str: ...\ndef to_json(\n self : DataFrame,\n path_or_buf : PathLike[str] | WriteBuffer[bytes] | WriteBuffer[str] | str,\n *,\n orient : Literal['columns', 'index', 'records', 'split', 'table', 'values'] | None = ...,\n date_format : Literal['epoch', 'iso'] | None = ...,\n double_precision: int = ...,\n force_ascii : bool = ...,\n date_unit : TimeUnit = ...,\n default_handler : ((Any) -> JSONSerializable) | None = ...,\n lines : bool = ...,\n compression : CompressionOptions = ...,\n index : bool | None = ...,\n indent : int | None = ...,\n storage_options : dict[Unknown, Unknown] | None = ...,\n mode : Literal['w'] = ...\n) -> None: ...", "kind": 2, "label": "to_json", "sortText": "0z.9999.to_json"}, {"detail": "\n@overload\ndef to_latex(\n self : DataFrame,\n buf : PathLike[str] | WriteBuffer[str] | str,\n columns : list[str] | None = ...,\n header : bool | list[str] = ...,\n index : bool = ...,\n na_rep : str = ...,\n formatters : Unknown = ...,\n float_format : Unknown = ...,\n sparsify : bool | None = ...,\n index_names : bool = ...,\n bold_rows : bool = ...,\n column_format : str | None = ...,\n longtable : bool | None = ...,\n escape : bool | None = ...,\n encoding : str | None = ...,\n decimal : str = ...,\n multicolumn : bool | None = ...,\n multicolumn_format: str | None = ...,\n multirow : bool | None = ...,\n caption : str | tuple[str, str] | None = ...,\n label : str | None = ...,\n position : str | None = ...\n) -> None: ...\ndef to_latex(\n self : DataFrame,\n buf : None = None,\n columns : list[str] | None = ...,\n header : bool | list[str] = ...,\n index : bool = ...,\n na_rep : str = ...,\n formatters : Unknown = ...,\n float_format : Unknown = ...,\n sparsify : bool | None = ...,\n index_names : bool = ...,\n bold_rows : bool = ...,\n column_format : str | None = ...,\n longtable : bool | None = ...,\n escape : bool | None = ...,\n encoding : str | None = ...,\n decimal : str = ...,\n multicolumn : bool | None = ...,\n multicolumn_format: str | None = ...,\n multirow : bool | None = ...,\n caption : str | tuple[str, str] | None = ...,\n label : str | None = ...,\n position : str | None = ...\n) -> str: ...", "kind": 2, "label": "to_latex", "sortText": "0z.9999.to_latex"}, {"detail": "\n@overload\ndef to_markdown(\n self : DataFrame,\n buf : PathLike[str] | WriteBuffer[str] | str,\n *,\n mode : FileWriteMode = ...,\n index : bool = ...,\n storage_options: StorageOptions = ...,\n **kwargs : Any\n) -> None: ...\ndef to_markdown(\n self : DataFrame,\n buf : None = None,\n *,\n mode : Literal['a', 'a+', 'a+b', 'ab', 'at', 'w', 'w+', 'w+b', 'wb', 'wt', 'x', 'xb', 'xt'] | None = ...,\n index : bool = ...,\n storage_options: StorageOptions = ...,\n **kwargs : Any\n) -> str: ...", "kind": 2, "label": "to_markdown", "sortText": "0z.9999.to_markdown"}, {"detail": "def to_numpy(\n self : DataFrame,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = ...,\n copy : bool = False,\n na_value: Scalar = ...\n) -> ndarray[tuple[int, int]]: ...", "kind": 2, "label": "to_numpy", "sortText": "0z.9999.to_numpy"}, {"detail": "\n@overload\ndef to_orc(\n self : DataFrame,\n path : PathLike[str] | WriteBuffer[bytes] | str,\n *,\n engine : Literal['pyarrow'] = ...,\n index : bool | None = ...,\n engine_kwargs: dict[str, Any] | None = ...\n) -> None: ...\ndef to_orc(\n self : DataFrame,\n path : None = None,\n *,\n engine : Literal['pyarrow'] = ...,\n index : bool | None = ...,\n engine_kwargs: dict[str, Any] | None = ...\n) -> bytes: ...", "kind": 2, "label": "to_orc", "sortText": "0z.9999.to_orc"}, {"detail": "\n@overload\ndef to_parquet(\n self : DataFrame,\n path : PathLike[str] | WriteBuffer[bytes] | str,\n *,\n engine : ParquetEngine = ...,\n compression : Literal['brotli', 'gzip', 'lz4', 'snappy', 'zstd'] | None = ...,\n index : bool | None = ...,\n partition_cols : Sequence[Hashable] | None = ...,\n storage_options: StorageOptions = ...,\n **kwargs : Any\n) -> None: ...\ndef to_parquet(\n self : DataFrame,\n path : None = None,\n *,\n engine : ParquetEngine = ...,\n compression : Literal['brotli', 'gzip', 'lz4', 'snappy', 'zstd'] | None = ...,\n index : bool | None = ...,\n partition_cols : Sequence[Hashable] | None = ...,\n storage_options: StorageOptions = ...,\n **kwargs : Any\n) -> bytes: ...", "kind": 2, "label": "to_parquet", "sortText": "0z.9999.to_parquet"}, {"detail": "def to_period(\n self: DataFrame,\n freq: Day | Hour | Micro | Milli | Minute | MonthEnd | Nano | QuarterEnd | Second | Week | YearEnd | str | None = None,\n axis: Axis = 0,\n copy: bool = True\n) -> DataFrame: ...", "kind": 2, "label": "to_period", "sortText": "0z.9999.to_period"}, {"detail": "def to_pickle(\n self : DataFrame,\n path : PathLike[str] | WriteBuffer[bytes] | str,\n compression : CompressionOptions = 'infer',\n protocol : int = 5,\n storage_options: StorageOptions = ...\n) -> None: ...", "kind": 2, "label": "to_pickle", "sortText": "0z.9999.to_pickle"}, {"detail": "def to_records[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n index : bool = True,\n column_dtypes: Mapping[HashableT1, DTypeLike] | _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n index_dtypes : Mapping[HashableT2, DTypeLike] | _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None\n) -> recarray: ...", "kind": 2, "label": "to_records", "sortText": "0z.9999.to_records"}, {"detail": "def to_sql(\n self: DataFrame,\n name: str,\n con: Unknown,\n *,\n schema: str | None = None,\n if_exists: Literal['append', 'delete_rows', 'fail', 'replace'] = 'fail',\n index: bool = True,\n index_label: IndexLabel = None,\n chunksize: int | None = None,\n dtype: ExtensionDtype | Mapping[Hashable, ExtensionDtype | dtype[generic] | str | type[bool | complex | object | str]] | dtype[generic] | str | type[bool | complex | object | str] | None = None,\n method: Literal['multi'] | ((\n SQLTable,\n Any,\n list[str],\n Iterable[tuple[Any, ...]]\n) -> int | None) | None = None\n) -> int | None: ...", "kind": 2, "label": "to_sql", "sortText": "0z.9999.to_sql"}, {"detail": "\n@overload\ndef to_stata[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n path : PathLike[str] | WriteBuffer[bytes] | str,\n *,\n convert_dates : dict[HashableT1, StataDateFormat] | None = ...,\n write_index : bool = ...,\n byteorder : Literal['<', '>', 'big', 'little'] | None = ...,\n time_stamp : datetime | None = ...,\n data_label : str | None = ...,\n variable_labels: dict[HashableT2, str] | None = ...,\n version : Literal[117, 118, 119],\n convert_strl : SequenceNotStr[Hashable] | None = ...,\n compression : CompressionOptions = ...,\n storage_options: StorageOptions = ...,\n value_labels : dict[Hashable, dict[float, str]] | None = ...\n) -> None: ...\ndef to_stata[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n path : PathLike[str] | WriteBuffer[bytes] | str,\n *,\n convert_dates : dict[HashableT1, StataDateFormat] | None = ...,\n write_index : bool = ...,\n byteorder : Literal['<', '>', 'big', 'little'] | None = ...,\n time_stamp : datetime | None = ...,\n data_label : str | None = ...,\n variable_labels: dict[HashableT2, str] | None = ...,\n version : Literal[114, 117, 118, 119] | None = ...,\n convert_strl : None = None,\n compression : CompressionOptions = ...,\n storage_options: StorageOptions = ...,\n value_labels : dict[Hashable, dict[float, str]] | None = ...\n) -> None: ...", "kind": 2, "label": "to_stata", "sortText": "0z.9999.to_stata"}, {"detail": "\n@overload\ndef to_string[HashableT: Hashable](\n self : DataFrame,\n buf : PathLike[str] | WriteBuffer[str] | str,\n *,\n columns : Index | SequenceNotStr[Hashable] | Series | None = ...,\n col_space : dict[HashableT, int] | int | list[int] | None = ...,\n header : bool | list[str] | tuple[str, ...] = ...,\n index : bool = ...,\n na_rep : str = ...,\n formatters : Mapping[int | str, (...) -> Unknown] | list[(...) -> Unknown] | tuple[(...) -> Unknown, ...] | None = ...,\n float_format : ((float) -> str) | EngFormatter | str | None = ...,\n sparsify : bool | None = ...,\n index_names : bool = ...,\n justify : str | None = ...,\n max_rows : int | None = ...,\n max_cols : int | None = ...,\n show_dimensions: bool = ...,\n decimal : str = ...,\n line_width : int | None = ...,\n min_rows : int | None = ...,\n max_colwidth : int | None = ...,\n encoding : str | None = ...\n) -> None: ...\ndef to_string(\n self : DataFrame,\n buf : None = None,\n *,\n columns : Index | Sequence[Hashable] | Series | None = ...,\n col_space : dict[Hashable, int] | int | list[int] | None = ...,\n header : Sequence[str] | bool = ...,\n index : bool = ...,\n na_rep : str = ...,\n formatters : Mapping[int | str, (...) -> Unknown] | list[(...) -> Unknown] | tuple[(...) -> Unknown, ...] | None = ...,\n float_format : ((float) -> str) | EngFormatter | str | None = ...,\n sparsify : bool | None = ...,\n index_names : bool = ...,\n justify : str | None = ...,\n max_rows : int | None = ...,\n max_cols : int | None = ...,\n show_dimensions: bool = ...,\n decimal : str = ...,\n line_width : int | None = ...,\n min_rows : int | None = ...,\n max_colwidth : int | None = ...,\n encoding : str | None = ...\n) -> str: ...", "kind": 2, "label": "to_string", "sortText": "0z.9999.to_string"}, {"detail": "def to_timestamp(\n self: DataFrame,\n freq: Day | Hour | Micro | Milli | Minute | MonthEnd | Nano | QuarterEnd | Second | Week | YearEnd | str | None = None,\n how : ToTimestampHow = ...,\n axis: Axis = 0,\n copy: bool = True\n) -> DataFrame: ...", "kind": 2, "label": "to_timestamp", "sortText": "0z.9999.to_timestamp"}, {"detail": "def to_xarray(self: DataFrame) -> Unknown: ...", "kind": 2, "label": "to_xarray", "sortText": "0z.9999.to_xarray"}, {"detail": "\n@overload\ndef to_xml(\n self : DataFrame,\n path_or_buffer : PathLike[str] | WriteBuffer[bytes] | WriteBuffer[str] | str,\n index : bool = ...,\n root_name : str = ...,\n row_name : str = ...,\n na_rep : str | None = ...,\n attr_cols : SequenceNotStr[Hashable] | None = ...,\n elem_cols : SequenceNotStr[Hashable] | None = ...,\n namespaces : dict[str | None, str] | None = ...,\n prefix : str | None = ...,\n encoding : str = ...,\n xml_declaration: bool = ...,\n pretty_print : bool = ...,\n parser : XMLParsers = ...,\n stylesheet : PathLike[str] | ReadBuffer[bytes] | ReadBuffer[str] | str | None = ...,\n compression : CompressionOptions = ...,\n storage_options: StorageOptions = ...\n) -> None: ...\ndef to_xml(\n self : DataFrame,\n path_or_buffer : None = None,\n index : bool = ...,\n root_name : str | None = ...,\n row_name : str | None = ...,\n na_rep : str | None = ...,\n attr_cols : list[Hashable] | None = ...,\n elem_cols : list[Hashable] | None = ...,\n namespaces : dict[str | None, str] | None = ...,\n prefix : str | None = ...,\n encoding : str = ...,\n xml_declaration: bool | None = ...,\n pretty_print : bool | None = ...,\n parser : str | None = ...,\n stylesheet : PathLike[str] | ReadBuffer[bytes] | ReadBuffer[str] | str | None = ...,\n compression : CompressionOptions = ...,\n storage_options: StorageOptions = ...\n) -> str: ...", "kind": 2, "label": "to_xml", "sortText": "0z.9999.to_xml"}, {"detail": "def transform(\n self : DataFrame,\n func : AggFuncTypeFrame,\n axis : Axis = 0,\n *args : Any,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "transform", "sortText": "0z.9999.transform"}, {"detail": "def transpose(\n self : DataFrame,\n *args: Any,\n *,\n copy : bool = ...\n) -> DataFrame: ...", "kind": 2, "label": "transpose", "sortText": "0z.9999.transpose"}, {"detail": "def truediv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "truediv", "sortText": "0z.9999.truediv"}, {"detail": "def truncate(\n self : DataFrame,\n before: date | int | str | None = ...,\n after : date | int | str | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n copy : bool = ...\n) -> DataFrame: ...", "kind": 2, "label": "truncate", "sortText": "0z.9999.truncate"}, {"detail": "def tz_convert(\n self : DataFrame,\n tz : TimeZones,\n axis : Axis = 0,\n level: Hashable | None = None,\n copy : bool = True\n) -> DataFrame: ...", "kind": 2, "label": "tz_convert", "sortText": "0z.9999.tz_convert"}, {"detail": "def tz_localize(\n self : DataFrame,\n tz : TimeZones,\n axis : Axis = 0,\n level : Hashable | None = None,\n copy : builtins.bool = True,\n ambiguous : TimeAmbiguous = 'raise',\n nonexistent: TimeNonexistent = 'raise'\n) -> DataFrame: ...", "kind": 2, "label": "tz_localize", "sortText": "0z.9999.tz_localize"}, {"detail": "def unstack(\n self : DataFrame,\n level : IndexLabel = -1,\n fill_value: Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = None,\n sort : bool = True\n) -> DataFrame | Series: ...", "kind": 2, "label": "unstack", "sortText": "0z.9999.unstack"}, {"detail": "def update(\n self : DataFrame,\n other : DataFrame | Series,\n join : Literal['left'] = 'left',\n overwrite : bool = True,\n filter_func: ((...) -> Unknown) | None = ...,\n errors : IgnoreRaise = 'ignore'\n) -> None: ...", "kind": 2, "label": "update", "sortText": "0z.9999.update"}, {"detail": "\n@overload\ndef value_counts(\n self : DataFrame,\n subset : Sequence[Hashable] | None = ...,\n normalize: Literal[False] = False,\n sort : bool = ...,\n ascending: bool = ...,\n dropna : bool = ...\n) -> Series[int]: ...\ndef value_counts(\n self : DataFrame,\n normalize: Literal[True],\n subset : Sequence[Hashable] | None = ...,\n sort : bool = ...,\n ascending: bool = ...,\n dropna : bool = ...\n) -> Series[float]: ...", "kind": 2, "label": "value_counts", "sortText": "0z.9999.value_counts"}, {"detail": "ndarray[tuple[int, int]]", "kind": 5, "label": "values", "sortText": "0z.9999.values"}, {"detail": "def var(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = 0,\n skipna : bool | None = True,\n ddof : int = 1,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "var", "sortText": "0z.9999.var"}, {"detail": "\n@overload\ndef where(\n self : DataFrame,\n cond : ((DataFrame) -> DataFrame) | ((Any) -> builtins.bool) | DataFrame | Series | ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other : ((...) -> DataFrame | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64) | DataFrame | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 = ...,\n *,\n inplace: Literal[True],\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n level : Hashable | None = None\n) -> None: ...\ndef where(\n self : DataFrame,\n cond : ((DataFrame) -> DataFrame) | ((Any) -> builtins.bool) | DataFrame | Series | ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other : ((...) -> DataFrame | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64) | DataFrame | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 = ...,\n *,\n inplace: Literal[False] = False,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n level : Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "where", "sortText": "0z.9999.where"}, {"detail": "def xs(\n self : DataFrame,\n key : IndexLabel,\n axis : Axis = 0,\n level : Hashable | Sequence[Hashable] | None = None,\n drop_level: bool = True\n) -> DataFrame | Series: ...", "kind": 2, "label": "xs", "sortText": "0z.9999.xs"}, {"detail": "def __abs__(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "__abs__", "sortText": "3z.9999.__abs__"}, {"detail": "def __add__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__add__", "sortText": "3z.9999.__add__"}, {"detail": "def __and__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__and__", "sortText": "3z.9999.__and__"}, {"detail": "def __array__(\n self : DataFrame,\n dtype: Unknown = ...\n) -> ndarray: ...", "kind": 2, "label": "__array__", "sortText": "3z.9999.__array__"}, {"detail": "int", "kind": 5, "label": "__array_priority__", "sortText": "3z.9999.__array_priority__"}, {"detail": "def __arrow_c_stream__(\n self : DataFrame,\n requested_schema: object | None = None\n) -> object: ...", "kind": 2, "label": "__arrow_c_stream__", "sortText": "3z.9999.__arrow_c_stream__"}, {"detail": "def __bool__(self: DataFrame) -> NoReturn: ...", "kind": 2, "label": "__bool__", "sortText": "3z.9999.__bool__"}, {"detail": "def __contains__(\n self: DataFrame,\n key: Unknown\n) -> bool: ...", "kind": 2, "label": "__contains__", "sortText": "3z.9999.__contains__"}, {"detail": "def __copy__(\n self: DataFrame,\n deep: bool = ...\n) -> DataFrame: ...", "kind": 2, "label": "__copy__", "sortText": "3z.9999.__copy__"}, {"detail": "def __deepcopy__(\n self: DataFrame,\n memo: MutableMapping[int, Any] | None = None\n) -> DataFrame: ...", "kind": 2, "label": "__deepcopy__", "sortText": "3z.9999.__deepcopy__"}, {"detail": "def __delitem__(\n self: DataFrame,\n idx: Hashable\n) -> None: ...", "kind": 2, "label": "__delitem__", "sortText": "3z.9999.__delitem__"}, {"detail": "def __divmod__(\n self: DataFrame,\n other: Any\n) -> tuple[DataFrame, DataFrame]: ...", "kind": 2, "label": "__divmod__", "sortText": "3z.9999.__divmod__"}, {"detail": "def __eq__(\n self: DataFrame,\n other: object\n) -> DataFrame: ...", "kind": 2, "label": "__eq__", "sortText": "3z.9999.__eq__"}, {"detail": "def __finalize__(\n self : DataFrame,\n other : Unknown,\n method : Unknown = ...,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "__finalize__", "sortText": "3z.9999.__finalize__"}, {"detail": "def __floordiv__(\n self: DataFrame,\n other: DataFrame | Sequence[float] | Series[float] | Series[int] | float\n) -> DataFrame: ...", "kind": 2, "label": "__floordiv__", "sortText": "3z.9999.__floordiv__"}, {"detail": "def __ge__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__ge__", "sortText": "3z.9999.__ge__"}, {"detail": "def __getattr__(\n self: DataFrame,\n name: str\n) -> Series: ...", "kind": 2, "label": "__getattr__", "sortText": "3z.9999.__getattr__"}, {"detail": "\n@overload\ndef __getitem__(\n self: DataFrame,\n key: Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | tuple[Hashable, ...]\n) -> Series: ...\ndef __getitem__(\n self: DataFrame,\n key: Iterable[Hashable] | slice[Any, Any, Any]\n) -> DataFrame: ...\ndef __getitem__(\n self: DataFrame,\n key: Hashable\n) -> Series: ...", "kind": 2, "label": "__getitem__", "sortText": "3z.9999.__getitem__"}, {"detail": "def __gt__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__gt__", "sortText": "3z.9999.__gt__"}, {"detail": "None", "kind": 5, "label": "__hash__", "sortText": "3z.9999.__hash__"}, {"detail": "def __invert__(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "__invert__", "sortText": "3z.9999.__invert__"}, {"detail": "def __iter__(self: DataFrame) -> Iterator[Hashable]: ...", "kind": 2, "label": "__iter__", "sortText": "3z.9999.__iter__"}, {"detail": "def __le__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__le__", "sortText": "3z.9999.__le__"}, {"detail": "def __len__(self: DataFrame) -> int: ...", "kind": 2, "label": "__len__", "sortText": "3z.9999.__len__"}, {"detail": "def __lt__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__lt__", "sortText": "3z.9999.__lt__"}, {"detail": "\n@overload\ndef __matmul__(\n self: DataFrame,\n other: DataFrame\n) -> DataFrame: ...\ndef __matmul__(\n self: DataFrame,\n other: Series\n) -> Series: ...\ndef __matmul__(\n self: DataFrame,\n other: ndarray[tuple[Any, ...], dtype[np_num]]\n) -> DataFrame: ...", "kind": 2, "label": "__matmul__", "sortText": "3z.9999.__matmul__"}, {"detail": "def __mod__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__mod__", "sortText": "3z.9999.__mod__"}, {"detail": "def __mul__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__mul__", "sortText": "3z.9999.__mul__"}, {"detail": "def __ne__(\n self: DataFrame,\n other: object\n) -> DataFrame: ...", "kind": 2, "label": "__ne__", "sortText": "3z.9999.__ne__"}, {"detail": "def __neg__(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "__neg__", "sortText": "3z.9999.__neg__"}, {"detail": "\n@overload\ndef __new__[Self@DataFrame: DataFrame](\n cls : type[Self@DataFrame],\n data : DataFrame | Index | Iterable[Index | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | tuple[Hashable, ListLikeU]] | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | None = None,\n index : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n columns: ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n dtype : ExtensionDtype | dtype[generic] | str | type[bool | complex | object | str] | None = None,\n copy : bool | None = None\n) -> Self@DataFrame: ...\ndef __new__[Self@DataFrame: DataFrame](\n cls : type[Self@DataFrame],\n data : Scalar,\n index : Axes,\n columns: Axes,\n dtype : ExtensionDtype | dtype[generic] | str | type[bool | complex | object | str] | None = None,\n copy : bool | None = None\n) -> Self@DataFrame: ...", "kind": 3, "label": "__new__", "sortText": "3z.9999.__new__"}, {"detail": "def __nonzero__(self: DataFrame) -> None: ...", "kind": 2, "label": "__nonzero__", "sortText": "3z.9999.__nonzero__"}, {"detail": "def __or__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__or__", "sortText": "3z.9999.__or__"}, {"detail": "def __pos__(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "__pos__", "sortText": "3z.9999.__pos__"}, {"detail": "def __pow__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__pow__", "sortText": "3z.9999.__pow__"}, {"detail": "def __radd__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__radd__", "sortText": "3z.9999.__radd__"}, {"detail": "def __rand__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__rand__", "sortText": "3z.9999.__rand__"}, {"detail": "def __rdivmod__(\n self: DataFrame,\n other: Any\n) -> tuple[DataFrame, DataFrame]: ...", "kind": 2, "label": "__rdivmod__", "sortText": "3z.9999.__rdivmod__"}, {"detail": "def __rfloordiv__(\n self: DataFrame,\n other: DataFrame | Sequence[float] | Series[float] | Series[int] | float\n) -> DataFrame: ...", "kind": 2, "label": "__rfloordiv__", "sortText": "3z.9999.__rfloordiv__"}, {"detail": "def __rmatmul__(\n self: DataFrame,\n other: ndarray[tuple[Any, ...], dtype[np_num]]\n) -> DataFrame: ...", "kind": 2, "label": "__rmatmul__", "sortText": "3z.9999.__rmatmul__"}, {"detail": "def __rmod__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__rmod__", "sortText": "3z.9999.__rmod__"}, {"detail": "def __rmul__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__rmul__", "sortText": "3z.9999.__rmul__"}, {"detail": "def __ror__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__ror__", "sortText": "3z.9999.__ror__"}, {"detail": "def __round__(\n self : DataFrame,\n decimals: int = ...\n) -> DataFrame: ...", "kind": 2, "label": "__round__", "sortText": "3z.9999.__round__"}, {"detail": "def __rpow__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__rpow__", "sortText": "3z.9999.__rpow__"}, {"detail": "def __rsub__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__rsub__", "sortText": "3z.9999.__rsub__"}, {"detail": "def __rtruediv__(\n self: DataFrame,\n other: DataFrame | Sequence[Unknown] | Series | float\n) -> DataFrame: ...", "kind": 2, "label": "__rtruediv__", "sortText": "3z.9999.__rtruediv__"}, {"detail": "def __rxor__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__rxor__", "sortText": "3z.9999.__rxor__"}, {"detail": "def __setattr__(\n self: DataFrame,\n name: str,\n value: Unknown\n) -> None: ...", "kind": 2, "label": "__setattr__", "sortText": "3z.9999.__setattr__"}, {"detail": "\n@overload\ndef __setitem__(\n self: DataFrame,\n idx: Index | Series[int] | int | list[int] | ndarray[tuple[Any, ...], dtype[integer]] | slice[Any, Any, Any] | tuple[IndexType, IndexType] | tuple[IndexType, int] | tuple[int, IndexType] | tuple[int, int],\n value: DataFrame | IndexOpsMixin | Mapping[Hashable, NAType | NaTType | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | NAType | NaTType | Sequence[Scalar] | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64 | None\n) -> None: ...\ndef __setitem__[ScalarT: Scalar](\n self: DataFrame,\n idx: Series[builtins.bool] | int | integer | list[builtins.bool] | list[ScalarT] | ndarray[tuple[Any, ...], dtype[numpy.bool]] | signedinteger[_NBitIntP] | signedinteger[_8Bit] | signedinteger | slice[Any, Any, Any] | str | str_ | unsignedinteger | tuple[IndexOpsMixin | Sequence[Scalar] | Series[builtins.bool] | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | list[builtins.bool] | ndarray[tuple[Any, ...], dtype[numpy.bool]] | slice[Any, Any, Any] | str | timedelta | timedelta64, ...],\n value: DataFrame | ExtensionArray | IndexOpsMixin | Mapping[Hashable, NAType | NaTType | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | NAType | NaTType | Sequence[Scalar] | Sequence[Sequence[Scalar]] | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64 | None\n) -> None: ...\ndef __setitem__(\n self: DataFrame,\n idx: tuple[tuple[IndexOpsMixin | Sequence[Scalar] | Series[builtins.bool] | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | list[builtins.bool] | ndarray[tuple[Any, ...], dtype[numpy.bool]] | slice[Any, Any, Any] | str | timedelta | timedelta64, ...], Hashable],\n value: ExtensionArray | IndexOpsMixin | Mapping[Hashable, NAType | NaTType | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | NAType | NaTType | Sequence[Scalar] | Sequence[Sequence[Scalar]] | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64 | None\n) -> None: ...\ndef __setitem__(\n self: DataFrame,\n idx: DataFrame | IndexOpsMixin,\n value: ExtensionArray | IndexOpsMixin | Mapping[Hashable, NAType | NaTType | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | NAType | NaTType | Sequence[Scalar] | Sequence[Sequence[Scalar]] | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64 | None\n) -> None: ...", "kind": 2, "label": "__setitem__", "sortText": "3z.9999.__setitem__"}, {"detail": "def __sub__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__sub__", "sortText": "3z.9999.__sub__"}, {"detail": "def __truediv__(\n self: DataFrame,\n other: DataFrame | Sequence[Unknown] | Series | float\n) -> DataFrame: ...", "kind": 2, "label": "__truediv__", "sortText": "3z.9999.__truediv__"}, {"detail": "def __xor__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__xor__", "sortText": "3z.9999.__xor__"}]}} {"suite": "pandas", "label": "edit dataframe then complete (edit+completion)", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 17, "character": 39, "iteration": 4, "result": {"isIncomplete": false, "items": [{"detail": "str", "kind": 5, "label": "Name", "sortText": "0.9999.Name"}, {"detail": "DataFrame", "kind": 5, "label": "T", "sortText": "0z.9999.T"}, {"detail": "def abs(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "abs", "sortText": "0z.9999.abs"}, {"detail": "def add(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "add", "sortText": "0z.9999.add"}, {"detail": "def add_prefix(\n self : DataFrame,\n prefix: str,\n axis : Literal['columns', 'index', 0, 1] | None = None\n) -> DataFrame: ...", "kind": 2, "label": "add_prefix", "sortText": "0z.9999.add_prefix"}, {"detail": "def add_suffix(\n self : DataFrame,\n suffix: str,\n axis : Literal['columns', 'index', 0, 1] | None = None\n) -> DataFrame: ...", "kind": 2, "label": "add_suffix", "sortText": "0z.9999.add_suffix"}, {"detail": "\n@overload\ndef aggregate(\n self : DataFrame,\n func : ((...) -> Unknown) | Mapping[Unknown, ((...) -> Unknown) | str | ufunc] | str | ufunc,\n axis : Axis = 0,\n **kwargs: Any\n) -> Series: ...\ndef aggregate(\n self : DataFrame,\n func : Mapping[Unknown, ((...) -> Unknown) | list[((...) -> Unknown) | str | ufunc] | str | ufunc] | list[AggFuncTypeBase] | None = ...,\n axis : Axis = 0,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "agg", "sortText": "0z.9999.agg"}, {"detail": "\n@overload\ndef aggregate(\n self : DataFrame,\n func : ((...) -> Unknown) | Mapping[Unknown, ((...) -> Unknown) | str | ufunc] | str | ufunc,\n axis : Axis = 0,\n **kwargs: Any\n) -> Series: ...\ndef aggregate(\n self : DataFrame,\n func : Mapping[Unknown, ((...) -> Unknown) | list[((...) -> Unknown) | str | ufunc] | str | ufunc] | list[AggFuncTypeBase] | None = ...,\n axis : Axis = 0,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "aggregate", "sortText": "0z.9999.aggregate"}, {"detail": "def align[NDFrameT: NDFrame](\n self : DataFrame,\n other : NDFrameT,\n join : AlignJoin = 'outer',\n axis : Literal['columns', 'index', 0, 1] | None = None,\n level : Hashable | None = None,\n copy : bool = True,\n fill_value: NAType | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...\n) -> tuple[DataFrame, NDFrameT]: ...", "kind": 2, "label": "align", "sortText": "0z.9999.align"}, {"detail": "\n@overload\ndef all(\n self : DataFrame,\n axis : None,\n bool_only: builtins.bool | None = ...,\n skipna : builtins.bool = ...,\n **kwargs : Any\n) -> numpy.bool: ...\ndef all(\n self : DataFrame,\n axis : Axis = 0,\n bool_only: builtins.bool | None = ...,\n skipna : builtins.bool = ...,\n **kwargs : Any\n) -> Series[builtins.bool]: ...", "kind": 2, "label": "all", "sortText": "0z.9999.all"}, {"detail": "\n@overload\ndef any(\n self : DataFrame,\n *,\n axis : None,\n bool_only: builtins.bool | None = ...,\n skipna : builtins.bool = ...,\n **kwargs : Any\n) -> numpy.bool: ...\ndef any(\n self : DataFrame,\n *,\n axis : Axis = 0,\n bool_only: builtins.bool | None = ...,\n skipna : builtins.bool = ...,\n **kwargs : Any\n) -> Series[builtins.bool]: ...", "kind": 2, "label": "any", "sortText": "0z.9999.any"}, {"detail": "\n@overload\ndef apply(\n self : DataFrame,\n f : (...) -> Index | MutableSequence[Any] | Series | ndarray[tuple[int]] | tuple[Any, ...],\n axis : AxisIndex = ...,\n raw : bool = ...,\n result_type: None = None,\n args : Any = ...,\n **kwargs : Any\n) -> DataFrame: ...\ndef apply[S2: SeriesDType](\n self : DataFrame,\n f : (...) -> NAType | S2,\n axis : AxisIndex = ...,\n raw : bool = ...,\n result_type: None = None,\n args : Any = ...,\n **kwargs : Any\n) -> Series[S2]: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Mapping[Any, Any],\n axis : AxisIndex = ...,\n raw : bool = ...,\n result_type: None = None,\n args : Any = ...,\n **kwargs : Any\n) -> Series: ...\ndef apply[S2: SeriesDType](\n self : DataFrame,\n f : (...) -> NAType | S2,\n axis : Axis = 0,\n raw : bool = ...,\n args : Any = ...,\n *,\n result_type: Literal['expand', 'reduce'],\n **kwargs : Any\n) -> Series[S2]: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Index | Mapping[Any, Any] | MutableSequence[Any] | Series | ndarray[tuple[int]] | tuple[Any, ...],\n axis : Axis = 0,\n raw : bool = ...,\n args : Any = ...,\n *,\n result_type: Literal['expand'],\n **kwargs : Any\n) -> DataFrame: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Index | Mapping[Any, Any] | MutableSequence[Any] | ndarray[tuple[int]] | tuple[Any, ...],\n axis : Axis = 0,\n raw : bool = ...,\n args : Any = ...,\n *,\n result_type: Literal['reduce'],\n **kwargs : Any\n) -> Series: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Index | Mapping[Any, Any] | MutableSequence[Any] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray[tuple[int]] | str | timedelta | timedelta64 | tuple[Any, ...],\n axis : Axis = 0,\n raw : bool = ...,\n args : Any = ...,\n *,\n result_type: Literal['broadcast'],\n **kwargs : Any\n) -> DataFrame: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Series,\n axis : AxisIndex = 0,\n raw : bool = ...,\n args : Any = ...,\n *,\n result_type: Literal['reduce'],\n **kwargs : Any\n) -> Series: ...\ndef apply[S2: SeriesDType](\n self : DataFrame,\n f : (...) -> NAType | S2,\n raw : bool = ...,\n result_type: None = None,\n args : Any = ...,\n *,\n axis : AxisColumn,\n **kwargs : Any\n) -> Series[S2]: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Index | Mapping[Any, Any] | MutableSequence[Any] | ndarray[tuple[int]] | tuple[Any, ...],\n raw : bool = ...,\n result_type: None = None,\n args : Any = ...,\n *,\n axis : AxisColumn,\n **kwargs : Any\n) -> Series: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Series,\n raw : bool = ...,\n result_type: None = None,\n args : Any = ...,\n *,\n axis : AxisColumn,\n **kwargs : Any\n) -> DataFrame: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Series,\n raw : bool = ...,\n args : Any = ...,\n *,\n axis : AxisColumn,\n result_type: Literal['reduce'],\n **kwargs : Any\n) -> DataFrame: ...", "kind": 2, "label": "apply", "sortText": "0z.9999.apply"}, {"detail": "def asfreq(\n self : DataFrame,\n freq : Frequency,\n method : Literal['backfill', 'bfill', 'ffill', 'pad'] | None = None,\n how : Literal['end', 'start'] | None = ...,\n normalize : bool = False,\n fill_value: Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "asfreq", "sortText": "0z.9999.asfreq"}, {"detail": "def asof(\n self : DataFrame,\n where : ExtensionArray | Index | Sequence[Scalar] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64,\n subset: Hashable | list[Hashable] | None = None\n) -> DataFrame: ...", "kind": 2, "label": "asof", "sortText": "0z.9999.asof"}, {"detail": "def assign(\n self: DataFrame,\n **kwargs: IntoColumn\n) -> DataFrame: ...", "kind": 2, "label": "assign", "sortText": "0z.9999.assign"}, {"detail": "def astype(\n self : DataFrame,\n dtype : Literal[' DataFrame: ...", "kind": 2, "label": "astype", "sortText": "0z.9999.astype"}, {"detail": "_AtIndexerFrame", "kind": 5, "label": "at", "sortText": "0z.9999.at"}, {"detail": "def at_time(\n self: DataFrame,\n time: str | time,\n asof: bool = False,\n axis: Literal['columns', 'index', 0, 1] | None = 0\n) -> DataFrame: ...", "kind": 2, "label": "at_time", "sortText": "0z.9999.at_time"}, {"detail": "dict[Hashable | None, Any]", "kind": 5, "label": "attrs", "sortText": "0z.9999.attrs"}, {"detail": "list[Index]", "kind": 5, "label": "axes", "sortText": "0z.9999.axes"}, {"detail": "def between_time(\n self : DataFrame,\n start_time: str | time,\n end_time : str | time,\n inclusive : IntervalClosedType = 'both',\n axis : Literal['columns', 'index', 0, 1] | None = 0\n) -> DataFrame: ...", "kind": 2, "label": "between_time", "sortText": "0z.9999.between_time"}, {"detail": "\n@overload\ndef bfill(\n self : DataFrame,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n inplace : Literal[True],\n limit : int | None = ...,\n limit_area: Literal['inside', 'outside'] | None = ...\n) -> None: ...\ndef bfill(\n self : DataFrame,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n inplace : Literal[False] = False,\n limit : int | None = ...,\n limit_area: Literal['inside', 'outside'] | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "bfill", "sortText": "0z.9999.bfill"}, {"detail": "def bool(self: DataFrame) -> bool: ...", "kind": 2, "label": "bool", "sortText": "0z.9999.bool"}, {"detail": "\n@overload\ndef boxplot(\n self : DataFrame,\n by : None = None,\n ax : Unknown = None,\n fontsize : float | str | None = None,\n rot : float = 0,\n grid : bool = True,\n figsize : tuple[float, float] | None = None,\n layout : tuple[int, int] | None = None,\n *,\n return_type: Literal['axes'] | None = None,\n backend : str | None = None,\n **kwargs : Any\n) -> Unknown: ...\ndef boxplot(\n self : DataFrame,\n by : None = None,\n ax : Unknown = None,\n fontsize : float | str | None = None,\n rot : float = 0,\n grid : bool = True,\n figsize : tuple[float, float] | None = None,\n layout : tuple[int, int] | None = None,\n *,\n return_type: Literal['dict'],\n backend : str | None = None,\n **kwargs : Any\n) -> dict[str, Unknown]: ...\ndef boxplot(\n self : DataFrame,\n by : None = None,\n ax : Unknown = None,\n fontsize : float | str | None = None,\n rot : float = 0,\n grid : bool = True,\n figsize : tuple[float, float] | None = None,\n layout : tuple[int, int] | None = None,\n *,\n return_type: Literal['both'],\n backend : str | None = None,\n **kwargs : Any\n) -> _BoxPlotT: ...\ndef boxplot(\n self : DataFrame,\n by : Hashable | MutableSequence[Unknown] | ndarray[tuple[int]] | range | tuple[Unknown, ...],\n ax : Unknown = None,\n fontsize : float | str | None = None,\n rot : float = 0,\n grid : bool = True,\n figsize : tuple[float, float] | None = None,\n layout : tuple[int, int] | None = None,\n *,\n return_type: None = None,\n backend : str | None = None,\n **kwargs : Any\n) -> Unknown: ...\ndef boxplot(\n self : DataFrame,\n by : Hashable | MutableSequence[Unknown] | ndarray[tuple[int]] | range | tuple[Unknown, ...],\n ax : Unknown = None,\n fontsize : float | str | None = None,\n rot : float = 0,\n grid : bool = True,\n figsize : tuple[float, float] | None = None,\n layout : tuple[int, int] | None = None,\n *,\n return_type: Literal['axes', 'both', 'dict'],\n backend : str | None = None,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "boxplot", "sortText": "0z.9999.boxplot"}, {"detail": "\n@overload\ndef clip(\n self : DataFrame,\n lower : float | None = ...,\n upper : float | None = ...,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n inplace : Literal[False] = False,\n **kwargs: Any\n) -> DataFrame: ...\ndef clip(\n self : DataFrame,\n lower : AnyArrayLike = ...,\n upper : ExtensionArray | Index | Series | ndarray | None = ...,\n *,\n axis : Axis = ...,\n inplace : Literal[False] = False,\n **kwargs: Any\n) -> DataFrame: ...\ndef clip(\n self : DataFrame,\n lower : ExtensionArray | Index | Series | ndarray | None = ...,\n upper : AnyArrayLike = ...,\n *,\n axis : Axis = ...,\n inplace : Literal[False] = False,\n **kwargs: Any\n) -> DataFrame: ...\ndef clip(\n self : DataFrame,\n lower : None = None,\n upper : None = None,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n inplace : Literal[True],\n **kwargs: Any\n) -> DataFrame: ...\ndef clip(\n self : DataFrame,\n lower : float | None = ...,\n upper : float | None = ...,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n inplace : Literal[True],\n **kwargs: Any\n) -> None: ...\ndef clip(\n self : DataFrame,\n lower : AnyArrayLike = ...,\n upper : ExtensionArray | Index | Series | ndarray | None = ...,\n *,\n axis : Axis = ...,\n inplace : Literal[True],\n **kwargs: Any\n) -> None: ...\ndef clip(\n self : DataFrame,\n lower : ExtensionArray | Index | Series | ndarray | None = ...,\n upper : AnyArrayLike = ...,\n *,\n axis : Axis = ...,\n inplace : Literal[True],\n **kwargs: Any\n) -> None: ...", "kind": 2, "label": "clip", "sortText": "0z.9999.clip"}, {"detail": "Index[str]", "kind": 5, "label": "columns", "sortText": "0z.9999.columns"}, {"detail": "def combine(\n self : DataFrame,\n other : DataFrame,\n func : (...) -> Unknown,\n fill_value: Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = None,\n overwrite : bool = True\n) -> DataFrame: ...", "kind": 2, "label": "combine", "sortText": "0z.9999.combine"}, {"detail": "def combine_first(\n self: DataFrame,\n other: DataFrame\n) -> DataFrame: ...", "kind": 2, "label": "combine_first", "sortText": "0z.9999.combine_first"}, {"detail": "def compare(\n self : DataFrame,\n other : DataFrame,\n align_axis : Axis = 1,\n keep_shape : bool = False,\n keep_equal : bool = False,\n result_names: Suffixes = ...\n) -> DataFrame: ...", "kind": 2, "label": "compare", "sortText": "0z.9999.compare"}, {"detail": "def convert_dtypes(\n self : DataFrame,\n infer_objects : bool = True,\n convert_string : bool = True,\n convert_integer : bool = True,\n convert_boolean : bool = True,\n convert_floating: bool = True,\n dtype_backend : DtypeBackend = 'numpy_nullable'\n) -> DataFrame: ...", "kind": 2, "label": "convert_dtypes", "sortText": "0z.9999.convert_dtypes"}, {"detail": "def copy(\n self: DataFrame,\n deep: bool = True\n) -> DataFrame: ...", "kind": 2, "label": "copy", "sortText": "0z.9999.copy"}, {"detail": "def corr(\n self : DataFrame,\n method : Literal['kendall', 'pearson', 'spearman'] = 'pearson',\n min_periods : int = ...,\n numeric_only: bool = False\n) -> DataFrame: ...", "kind": 2, "label": "corr", "sortText": "0z.9999.corr"}, {"detail": "def corrwith(\n self : DataFrame,\n other : DataFrame | Series,\n axis : Literal['columns', 'index', 0, 1] | None = 0,\n drop : bool = False,\n method : Literal['kendall', 'pearson', 'spearman'] = 'pearson',\n numeric_only: bool = False\n) -> Series: ...", "kind": 2, "label": "corrwith", "sortText": "0z.9999.corrwith"}, {"detail": "def count(\n self : DataFrame,\n axis : Axis = 0,\n numeric_only: bool = False\n) -> Series[int]: ...", "kind": 2, "label": "count", "sortText": "0z.9999.count"}, {"detail": "def cov(\n self : DataFrame,\n min_periods : int | None = None,\n ddof : int = 1,\n numeric_only: bool = False\n) -> DataFrame: ...", "kind": 2, "label": "cov", "sortText": "0z.9999.cov"}, {"detail": "def cummax(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n skipna : bool = True,\n *args : Any,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "cummax", "sortText": "0z.9999.cummax"}, {"detail": "def cummin(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n skipna : bool = True,\n *args : Any,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "cummin", "sortText": "0z.9999.cummin"}, {"detail": "def cumprod(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n skipna : bool = True,\n *args : Any,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "cumprod", "sortText": "0z.9999.cumprod"}, {"detail": "def cumsum(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n skipna : bool = True,\n *args : Any,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "cumsum", "sortText": "0z.9999.cumsum"}, {"detail": "def describe(\n self : DataFrame,\n percentiles: list[float] | None = ...,\n include : Literal['all'] | list[Dtype] | None = ...,\n exclude : list[Dtype] | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "describe", "sortText": "0z.9999.describe"}, {"detail": "def diff(\n self : DataFrame,\n periods: int = 1,\n axis : Axis = 0\n) -> DataFrame: ...", "kind": 2, "label": "diff", "sortText": "0z.9999.diff"}, {"detail": "def truediv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "div", "sortText": "0z.9999.div"}, {"detail": "def truediv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "divide", "sortText": "0z.9999.divide"}, {"detail": "\n@overload\ndef dot(\n self: DataFrame,\n other: DataFrame | ExtensionArray | ndarray\n) -> DataFrame: ...\ndef dot(\n self: DataFrame,\n other: Series\n) -> Series: ...", "kind": 2, "label": "dot", "sortText": "0z.9999.dot"}, {"detail": "\n@overload\ndef drop(\n self : DataFrame,\n labels : Unknown = ...,\n *,\n axis : Unknown = ...,\n index : None,\n columns: Unknown = ...,\n level : Unknown = ...,\n inplace: Unknown = ...,\n errors : Unknown = ...\n) -> Never: ...\ndef drop(\n self : DataFrame,\n labels : Unknown = ...,\n *,\n axis : Unknown = ...,\n index : Unknown = ...,\n columns: None,\n level : Unknown = ...,\n inplace: Unknown = ...,\n errors : Unknown = ...\n) -> Never: ...\ndef drop(\n self : DataFrame,\n labels : None,\n *,\n axis : Unknown = ...,\n index : Unknown = ...,\n columns: Unknown = ...,\n level : Unknown = ...,\n inplace: Unknown = ...,\n errors : Unknown = ...\n) -> Never: ...\ndef drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable] = ...,\n columns: Hashable | Iterable[Hashable],\n level : Hashable | None = ...,\n inplace: Literal[True],\n errors : IgnoreRaise = ...\n) -> None: ...\ndef drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable],\n columns: Hashable | Iterable[Hashable] = ...,\n level : Hashable | None = ...,\n inplace: Literal[True],\n errors : IgnoreRaise = ...\n) -> None: ...\ndef drop(\n self : DataFrame,\n labels : Hashable | Index | Sequence[Hashable],\n *,\n axis : Axis = ...,\n index : None = None,\n columns: None = None,\n level : Hashable | None = ...,\n inplace: Literal[True],\n errors : IgnoreRaise = ...\n) -> None: ...\ndef drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable] = ...,\n columns: Hashable | Iterable[Hashable],\n level : Hashable | None = ...,\n inplace: Literal[False] = False,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...\ndef drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable],\n columns: Hashable | Iterable[Hashable] = ...,\n level : Hashable | None = ...,\n inplace: Literal[False] = False,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...\ndef drop(\n self : DataFrame,\n labels : Hashable | Index | Sequence[Hashable],\n *,\n axis : Axis = ...,\n index : None = None,\n columns: None = None,\n level : Hashable | None = ...,\n inplace: Literal[False] = False,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...", "kind": 2, "label": "drop", "sortText": "0z.9999.drop"}, {"detail": "\n@overload\ndef drop_duplicates(\n self : DataFrame,\n subset : Hashable | Iterable[Hashable] | None = ...,\n *,\n keep : DropKeep = ...,\n inplace : Literal[True],\n ignore_index: bool = ...\n) -> None: ...\ndef drop_duplicates(\n self : DataFrame,\n subset : Hashable | Iterable[Hashable] | None = ...,\n *,\n keep : DropKeep = ...,\n inplace : Literal[False] = False,\n ignore_index: bool = ...\n) -> DataFrame: ...", "kind": 2, "label": "drop_duplicates", "sortText": "0z.9999.drop_duplicates"}, {"detail": "def droplevel(\n self : DataFrame,\n level: Hashable | list[Hashable],\n axis : Axis = 0\n) -> DataFrame: ...", "kind": 2, "label": "droplevel", "sortText": "0z.9999.droplevel"}, {"detail": "\n@overload\ndef dropna(\n self : DataFrame,\n *,\n axis : Axis = 0,\n how : AnyAll = ...,\n thresh : int | None = ...,\n subset : Index | Sequence[Unknown] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray[tuple[int]] | str | timedelta | timedelta64 | None = ...,\n inplace : Literal[True],\n ignore_index: bool = ...\n) -> None: ...\ndef dropna(\n self : DataFrame,\n *,\n axis : Axis = 0,\n how : AnyAll = ...,\n thresh : int | None = ...,\n subset : Index | Sequence[Unknown] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray[tuple[int]] | str | timedelta | timedelta64 | None = ...,\n inplace : Literal[False] = False,\n ignore_index: bool = ...\n) -> DataFrame: ...", "kind": 2, "label": "dropna", "sortText": "0z.9999.dropna"}, {"detail": "Series", "kind": 5, "label": "dtypes", "sortText": "0z.9999.dtypes"}, {"detail": "def duplicated(\n self : DataFrame,\n subset: Hashable | Iterable[Hashable] | None = ...,\n keep : DropKeep = 'first'\n) -> Series: ...", "kind": 2, "label": "duplicated", "sortText": "0z.9999.duplicated"}, {"detail": "bool", "kind": 5, "label": "empty", "sortText": "0z.9999.empty"}, {"detail": "def eq(\n self : DataFrame,\n other: DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level: Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "eq", "sortText": "0z.9999.eq"}, {"detail": "def equals(\n self: DataFrame,\n other: DataFrame | Series\n) -> bool: ...", "kind": 2, "label": "equals", "sortText": "0z.9999.equals"}, {"detail": "\n@overload\ndef eval(\n self: DataFrame,\n expr: str,\n *,\n inplace: Literal[True],\n **kwargs: Any\n) -> None: ...\ndef eval(\n self : DataFrame,\n expr : str,\n *,\n inplace : Literal[False] = False,\n **kwargs: Any\n) -> DataFrame | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64: ...", "kind": 2, "label": "eval", "sortText": "0z.9999.eval"}, {"detail": "def ewm(\n self : DataFrame,\n com : float | None = ...,\n span : float | None = ...,\n halflife : float | None = ...,\n alpha : float | None = ...,\n min_periods: int = 0,\n adjust : bool = True,\n ignore_na : bool = False,\n axis : Axis = 0\n) -> ExponentialMovingWindow[DataFrame]: ...", "kind": 2, "label": "ewm", "sortText": "0z.9999.ewm"}, {"detail": "def expanding(\n self : DataFrame,\n min_periods: int = 1,\n axis : AxisIndex = 0,\n method : CalculationMethod = 'single'\n) -> Expanding[DataFrame]: ...", "kind": 2, "label": "expanding", "sortText": "0z.9999.expanding"}, {"detail": "def explode(\n self : DataFrame,\n column : Sequence[Hashable],\n ignore_index: bool = False\n) -> DataFrame: ...", "kind": 2, "label": "explode", "sortText": "0z.9999.explode"}, {"detail": "\n@overload\ndef ffill(\n self : DataFrame,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n inplace : Literal[True],\n limit : int | None = ...,\n limit_area: Literal['inside', 'outside'] | None = ...\n) -> None: ...\ndef ffill(\n self : DataFrame,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n inplace : Literal[False] = False,\n limit : int | None = ...,\n limit_area: Literal['inside', 'outside'] | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "ffill", "sortText": "0z.9999.ffill"}, {"detail": "\n@overload\ndef fillna(\n self : DataFrame,\n value : DataFrame | NAType | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | dict[Unknown, Unknown] | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n limit : int = ...,\n inplace: Literal[True]\n) -> None: ...\ndef fillna(\n self : DataFrame,\n value : DataFrame | NAType | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | dict[Unknown, Unknown] | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n limit : int = ...,\n inplace: Literal[False] = False\n) -> DataFrame: ...", "kind": 2, "label": "fillna", "sortText": "0z.9999.fillna"}, {"detail": "def filter(\n self : DataFrame,\n items: ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = ...,\n like : str | None = ...,\n regex: str | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = None\n) -> DataFrame: ...", "kind": 2, "label": "filter", "sortText": "0z.9999.filter"}, {"detail": "def first_valid_index(self: DataFrame) -> Scalar: ...", "kind": 2, "label": "first_valid_index", "sortText": "0z.9999.first_valid_index"}, {"detail": "def floordiv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | float | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "floordiv", "sortText": "0z.9999.floordiv"}, {"detail": "\n@overload\ndef from_dict(\n cls : type[DataFrame],\n data : dict[Any, Any],\n orient : Literal['index'],\n dtype : Literal[' DataFrame: ...\ndef from_dict(\n cls : type[DataFrame],\n data : dict[Any, Any],\n orient: Literal['columns', 'tight'] = ...,\n dtype : Literal[' DataFrame: ...", "kind": 2, "label": "from_dict", "sortText": "0z.9999.from_dict"}, {"detail": "def from_records[HashableT: Hashable](\n cls : type[DataFrame],\n data : Iterable[Mapping[HashableT, Any]] | Iterable[SequenceNotStr[Any]] | Mapping[HashableT, SequenceNotStr[Any]] | Mapping[HashableT, Any] | ndarray[tuple[int, int]],\n index : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | str | None = None,\n exclude : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n columns : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n coerce_float: bool = False,\n nrows : int | None = None\n) -> DataFrame: ...", "kind": 2, "label": "from_records", "sortText": "0z.9999.from_records"}, {"detail": "def ge(\n self : DataFrame,\n other: DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level: Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "ge", "sortText": "0z.9999.ge"}, {"detail": "\n@overload\ndef get(\n self : DataFrame,\n key : Hashable,\n default: None = None\n) -> Series | None: ...\ndef get[T](\n self: DataFrame,\n key: Hashable,\n default: T\n) -> Series | T: ...\ndef get(\n self : DataFrame,\n key : list[Hashable],\n default: None = None\n) -> DataFrame | None: ...\ndef get[T](\n self: DataFrame,\n key: list[Hashable],\n default: T\n) -> DataFrame | T: ...", "kind": 2, "label": "get", "sortText": "0z.9999.get"}, {"detail": "\n@overload\ndef groupby(\n self : DataFrame,\n by : Scalar,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Scalar, Literal[True]]: ...\ndef groupby(\n self : DataFrame,\n by : Scalar,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Scalar, Literal[False]]: ...\ndef groupby(\n self : DataFrame,\n by : DatetimeIndex,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Timestamp, Literal[True]]: ...\ndef groupby(\n self : DataFrame,\n by : DatetimeIndex,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Timestamp, Literal[False]]: ...\ndef groupby(\n self : DataFrame,\n by : TimedeltaIndex,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Timedelta, Literal[True]]: ...\ndef groupby(\n self : DataFrame,\n by : TimedeltaIndex,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Timedelta, Literal[False]]: ...\ndef groupby(\n self : DataFrame,\n by : PeriodIndex,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Period, Literal[True]]: ...\ndef groupby(\n self : DataFrame,\n by : PeriodIndex,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Period, Literal[False]]: ...\ndef groupby[IntervalT: Interval[Unknown]](\n self : DataFrame,\n by : IntervalIndex[IntervalT],\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[IntervalT, Literal[True]]: ...\ndef groupby[IntervalT: Interval[Unknown]](\n self : DataFrame,\n by : IntervalIndex[IntervalT],\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[IntervalT, Literal[False]]: ...\ndef groupby(\n self : DataFrame,\n by : ((...) -> Any) | Grouper | Mapping[Hashable | None, Any] | MultiIndex | list[((...) -> Any) | ufunc] | list[Grouper] | list[Index] | list[Mapping[Hashable | None, Any]] | list[Series] | list[ndarray] | list[Unknown] | ndarray | ufunc | tuple[Unknown, ...] | None = ...,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[tuple[Unknown, ...], Literal[True]]: ...\ndef groupby(\n self : DataFrame,\n by : ((...) -> Any) | Grouper | Mapping[Hashable | None, Any] | MultiIndex | list[((...) -> Any) | ufunc] | list[Grouper] | list[Index] | list[Mapping[Hashable | None, Any]] | list[Series] | list[ndarray] | list[Unknown] | ndarray | ufunc | tuple[Unknown, ...] | None = ...,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[tuple[Unknown, ...], Literal[False]]: ...\ndef groupby[SeriesByT: Interval[Timedelta | Timestamp | float | int] | Period | bool | bytes | complex | date | datetime | float | int | str | timedelta](\n self : DataFrame,\n by : Series[SeriesByT],\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[SeriesByT, Literal[True]]: ...\ndef groupby[SeriesByT: Interval[Timedelta | Timestamp | float | int] | Period | bool | bytes | complex | date | datetime | float | int | str | timedelta](\n self : DataFrame,\n by : Series[SeriesByT],\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[SeriesByT, Literal[False]]: ...\ndef groupby(\n self : DataFrame,\n by : CategoricalIndex | Index | Series,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Any, Literal[True]]: ...\ndef groupby(\n self : DataFrame,\n by : CategoricalIndex | Index | Series,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Any, Literal[False]]: ...", "kind": 2, "label": "groupby", "sortText": "0z.9999.groupby"}, {"detail": "def gt(\n self : DataFrame,\n other: DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level: Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "gt", "sortText": "0z.9999.gt"}, {"detail": "def head(\n self: DataFrame,\n n : int = 5\n) -> DataFrame: ...", "kind": 2, "label": "head", "sortText": "0z.9999.head"}, {"detail": "def hist(\n self : DataFrame,\n by : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | str | None = None,\n bins : int | list[Unknown] = 10,\n *,\n grid : bool = True,\n xlabelsize: float | str | None = None,\n xrot : float | None = None,\n ylabelsize: float | str | None = None,\n yrot : float | None = None,\n ax : Unknown = None,\n sharex : bool = False,\n sharey : bool = False,\n figsize : tuple[float, float] | None = None,\n layout : tuple[int, int] | None = None,\n backend : str | None = None,\n legend : bool = False,\n **kwargs : Any\n) -> ndarray[tuple[Any, ...], dtype[object_]]: ...", "kind": 2, "label": "hist", "sortText": "0z.9999.hist"}, {"detail": "_iAtIndexerFrame", "kind": 5, "label": "iat", "sortText": "0z.9999.iat"}, {"detail": "def idxmax(\n self : DataFrame,\n axis : Axis = 0,\n skipna : bool = True,\n numeric_only: bool = False\n) -> Series[int]: ...", "kind": 2, "label": "idxmax", "sortText": "0z.9999.idxmax"}, {"detail": "def idxmin(\n self : DataFrame,\n axis : Axis = 0,\n skipna : bool = True,\n numeric_only: bool = False\n) -> Series[int]: ...", "kind": 2, "label": "idxmin", "sortText": "0z.9999.idxmin"}, {"detail": "_iLocIndexerFrame[DataFrame]", "kind": 5, "label": "iloc", "sortText": "0z.9999.iloc"}, {"detail": "Index", "kind": 5, "label": "index", "sortText": "0z.9999.index"}, {"detail": "def infer_objects(\n self: DataFrame,\n copy: bool | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "infer_objects", "sortText": "0z.9999.infer_objects"}, {"detail": "def info(\n self : DataFrame,\n verbose : bool | None = ...,\n buf : WriteBuffer[str] | None = ...,\n max_cols : int | None = ...,\n memory_usage: Literal['deep'] | bool | None = ...,\n show_counts : bool | None = ...\n) -> None: ...", "kind": 2, "label": "info", "sortText": "0z.9999.info"}, {"detail": "def insert(\n self : DataFrame,\n loc : int,\n column : Hashable,\n value : Index | Sequence[Unknown] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray[tuple[int]] | str | timedelta | timedelta64 | None,\n allow_duplicates: bool = ...\n) -> None: ...", "kind": 2, "label": "insert", "sortText": "0z.9999.insert"}, {"detail": "\n@overload\ndef interpolate(\n self : DataFrame,\n method : InterpolateOptions = ...,\n *,\n axis : Axis = 0,\n limit : int | None = ...,\n limit_direction: Literal['backward', 'both', 'forward'] = ...,\n limit_area : Literal['inside', 'outside'] | None = ...,\n inplace : Literal[True],\n **kwargs : Any\n) -> None: ...\ndef interpolate(\n self : DataFrame,\n method : InterpolateOptions = ...,\n *,\n axis : Axis = 0,\n limit : int | None = ...,\n limit_direction: Literal['backward', 'both', 'forward'] = ...,\n limit_area : Literal['inside', 'outside'] | None = ...,\n inplace : Literal[False] = False,\n **kwargs : Any\n) -> DataFrame: ...", "kind": 2, "label": "interpolate", "sortText": "0z.9999.interpolate"}, {"detail": "def isetitem(\n self: DataFrame,\n loc: Sequence[int] | int,\n value: ExtensionArray | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | list[Any] | ndarray | str | timedelta | timedelta64\n) -> None: ...", "kind": 2, "label": "isetitem", "sortText": "0z.9999.isetitem"}, {"detail": "def isin(\n self: DataFrame,\n values: DataFrame | Iterable[Any] | Mapping[Hashable, Iterable[Any]]\n) -> DataFrame: ...", "kind": 2, "label": "isin", "sortText": "0z.9999.isin"}, {"detail": "def isna(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "isna", "sortText": "0z.9999.isna"}, {"detail": "def isnull(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "isnull", "sortText": "0z.9999.isnull"}, {"detail": "def items(self: DataFrame) -> Iterator[tuple[Hashable, Series]]: ...", "kind": 2, "label": "items", "sortText": "0z.9999.items"}, {"detail": "def iterrows(self: DataFrame) -> Iterator[tuple[Hashable, Series]]: ...", "kind": 2, "label": "iterrows", "sortText": "0z.9999.iterrows"}, {"detail": "\n@overload\ndef itertuples(\n self : DataFrame,\n index: bool = ...,\n name : str = ...\n) -> Iterator[_PandasNamedTuple]: ...\ndef itertuples(\n self : DataFrame,\n index: bool = ...,\n name : None = None\n) -> Iterator[tuple[Any, ...]]: ...", "kind": 2, "label": "itertuples", "sortText": "0z.9999.itertuples"}, {"detail": "def join(\n self : DataFrame,\n other : DataFrame | Series | list[DataFrame | Series],\n on : list[str] | str | None = ...,\n how : MergeHow = 'left',\n lsuffix : str = '',\n rsuffix : str = '',\n sort : bool = False,\n validate: Literal['1:1', '1:m', 'm:1', 'm:m', 'many_to_many', 'many_to_one', 'one_to_many', 'one_to_one'] | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "join", "sortText": "0z.9999.join"}, {"detail": "def keys(self: DataFrame) -> Index: ...", "kind": 2, "label": "keys", "sortText": "0z.9999.keys"}, {"detail": "def kurt(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "kurt", "sortText": "0z.9999.kurt"}, {"detail": "def kurtosis(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "kurtosis", "sortText": "0z.9999.kurtosis"}, {"detail": "def last_valid_index(self: DataFrame) -> Scalar: ...", "kind": 2, "label": "last_valid_index", "sortText": "0z.9999.last_valid_index"}, {"detail": "def le(\n self : DataFrame,\n other: DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level: Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "le", "sortText": "0z.9999.le"}, {"detail": "_LocIndexerFrame[DataFrame]", "kind": 5, "label": "loc", "sortText": "0z.9999.loc"}, {"detail": "def lt(\n self : DataFrame,\n other: DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level: Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "lt", "sortText": "0z.9999.lt"}, {"detail": "def map(\n self : DataFrame,\n func : (...) -> Unknown,\n na_action: Literal['ignore'] | None = None,\n **kwargs : Any\n) -> DataFrame: ...", "kind": 2, "label": "map", "sortText": "0z.9999.map"}, {"detail": "\n@overload\ndef mask(\n self : DataFrame,\n cond : ((DataFrame) -> DataFrame) | ((Any) -> builtins.bool) | DataFrame | Series | ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other : ((...) -> Unknown) | DataFrame | NAType | Series | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n *,\n inplace: Literal[True],\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n level : Hashable | None = None\n) -> None: ...\ndef mask(\n self : DataFrame,\n cond : ((DataFrame) -> DataFrame) | ((Any) -> builtins.bool) | DataFrame | Series | ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other : ((...) -> Unknown) | DataFrame | NAType | Series | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n *,\n inplace: Literal[False] = False,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n level : Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "mask", "sortText": "0z.9999.mask"}, {"detail": "\n@overload\ndef max(\n self : DataFrame,\n axis : None,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Scalar: ...\ndef max(\n self : DataFrame,\n axis : Axis = 0,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "max", "sortText": "0z.9999.max"}, {"detail": "\n@overload\ndef mean(\n self : DataFrame,\n axis : None,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Scalar: ...\ndef mean(\n self : DataFrame,\n axis : Axis = 0,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "mean", "sortText": "0z.9999.mean"}, {"detail": "\n@overload\ndef median(\n self : DataFrame,\n axis : None,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Scalar: ...\ndef median(\n self : DataFrame,\n axis : Axis = 0,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "median", "sortText": "0z.9999.median"}, {"detail": "def melt(\n self : DataFrame,\n id_vars : Sequence[Unknown] | ndarray | tuple[Unknown, ...] | None = ...,\n value_vars : Sequence[Unknown] | ndarray | tuple[Unknown, ...] | None = ...,\n var_name : Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = None,\n value_name : Scalar = 'value',\n col_level : int | str | None = ...,\n ignore_index: bool = True\n) -> DataFrame: ...", "kind": 2, "label": "melt", "sortText": "0z.9999.melt"}, {"detail": "def memory_usage(\n self : DataFrame,\n index: bool = True,\n deep : bool = False\n) -> Series: ...", "kind": 2, "label": "memory_usage", "sortText": "0z.9999.memory_usage"}, {"detail": "def merge(\n self : DataFrame,\n right : DataFrame | Series,\n how : MergeHow = 'inner',\n on : ExtensionArray | Hashable | Index | Sequence[Hashable] | Series | ndarray | None = ...,\n left_on : ExtensionArray | Hashable | Index | Sequence[Hashable] | Series | ndarray | None = ...,\n right_on : ExtensionArray | Hashable | Index | Sequence[Hashable] | Series | ndarray | None = ...,\n left_index : bool = False,\n right_index: bool = False,\n sort : bool = False,\n suffixes : Suffixes = ...,\n copy : bool = True,\n indicator : bool | str = False,\n validate : Literal['1:1', '1:m', 'm:1', 'm:m', 'many_to_many', 'many_to_one', 'one_to_many', 'one_to_one'] | None = None\n) -> DataFrame: ...", "kind": 2, "label": "merge", "sortText": "0z.9999.merge"}, {"detail": "\n@overload\ndef min(\n self : DataFrame,\n axis : None,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Scalar: ...\ndef min(\n self : DataFrame,\n axis : Axis = 0,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "min", "sortText": "0z.9999.min"}, {"detail": "def mod(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | float | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "mod", "sortText": "0z.9999.mod"}, {"detail": "def mode(\n self : DataFrame,\n axis : Axis = 0,\n numeric_only: bool = False,\n dropna : bool = True\n) -> Series: ...", "kind": 2, "label": "mode", "sortText": "0z.9999.mode"}, {"detail": "def mul(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "mul", "sortText": "0z.9999.mul"}, {"detail": "def mul(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "multiply", "sortText": "0z.9999.multiply"}, {"detail": "int", "kind": 5, "label": "ndim", "sortText": "0z.9999.ndim"}, {"detail": "def ne(\n self : DataFrame,\n other: DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level: Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "ne", "sortText": "0z.9999.ne"}, {"detail": "def nlargest(\n self : DataFrame,\n n : int,\n columns: list[str] | str,\n keep : NsmallestNlargestKeep = 'first'\n) -> DataFrame: ...", "kind": 2, "label": "nlargest", "sortText": "0z.9999.nlargest"}, {"detail": "def notna(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "notna", "sortText": "0z.9999.notna"}, {"detail": "def notnull(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "notnull", "sortText": "0z.9999.notnull"}, {"detail": "def nsmallest(\n self : DataFrame,\n n : int,\n columns: list[str] | str,\n keep : NsmallestNlargestKeep = 'first'\n) -> DataFrame: ...", "kind": 2, "label": "nsmallest", "sortText": "0z.9999.nsmallest"}, {"detail": "def nunique(\n self : DataFrame,\n axis : Axis = 0,\n dropna: bool = True\n) -> Series[int]: ...", "kind": 2, "label": "nunique", "sortText": "0z.9999.nunique"}, {"detail": "def pct_change(\n self : DataFrame,\n periods : int = 1,\n fill_method: None = None,\n freq : BaseOffset | str | timedelta | None = ...,\n fill_value : NAType | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "pct_change", "sortText": "0z.9999.pct_change"}, {"detail": "\n@overload\ndef pipe[**P, T](\n DataFrame,\n (\n DataFrame,\n ParamSpec(P)\n ) -> T,\n ParamSpec(P)\n) -> T: ...\ndef pipe[T](\n self: DataFrame,\n func: tuple[(...) -> T, str],\n *args: Any,\n **kwargs: Any\n) -> T: ...", "kind": 2, "label": "pipe", "sortText": "0z.9999.pipe"}, {"detail": "def pivot(\n self : DataFrame,\n *,\n columns: IndexLabel,\n index : IndexLabel = ...,\n values : IndexLabel = ...\n) -> DataFrame: ...", "kind": 2, "label": "pivot", "sortText": "0z.9999.pivot"}, {"detail": "def pivot_table(\n self : DataFrame,\n values : _PivotTableValuesTypes = None,\n index : _PivotTableIndexTypes = None,\n columns : _PivotTableColumnsTypes = None,\n aggfunc : Literal['count', 'max', 'mean', 'median', 'min', 'std', 'sum', 'var'] | ((Series) -> Unknown) | Mapping[Hashable, _PivotAggFunc] | Sequence[_PivotAggFunc] | ufunc = 'mean',\n fill_value : Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = None,\n margins : bool = False,\n dropna : bool = True,\n margins_name: str = 'All',\n observed : bool = True,\n sort : bool = True\n) -> DataFrame: ...", "kind": 2, "label": "pivot_table", "sortText": "0z.9999.pivot_table"}, {"detail": "PlotAccessor", "kind": 5, "label": "plot", "sortText": "0z.9999.plot"}, {"detail": "def pop(\n self: DataFrame,\n item: str\n) -> Series: ...", "kind": 2, "label": "pop", "sortText": "0z.9999.pop"}, {"detail": "def pow(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "pow", "sortText": "0z.9999.pow"}, {"detail": "def prod(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n skipna : bool | None = True,\n numeric_only: bool = False,\n min_count : int = 0,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "prod", "sortText": "0z.9999.prod"}, {"detail": "def prod(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n skipna : bool | None = True,\n numeric_only: bool = False,\n min_count : int = 0,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "product", "sortText": "0z.9999.product"}, {"detail": "\n@overload\ndef quantile(\n self : DataFrame,\n q : float = ...,\n axis : Axis = 0,\n numeric_only : bool = ...,\n interpolation: QuantileInterpolation = ...,\n method : CalculationMethod = ...\n) -> Series: ...\ndef quantile(\n self : DataFrame,\n q : Sequence[float] | ndarray[tuple[Any, ...], dtype[floating]],\n axis : Axis = 0,\n numeric_only : bool = ...,\n interpolation: QuantileInterpolation = ...,\n method : CalculationMethod = ...\n) -> DataFrame: ...", "kind": 2, "label": "quantile", "sortText": "0z.9999.quantile"}, {"detail": "\n@overload\ndef query(\n self : DataFrame,\n expr : str,\n *,\n parser : Literal['pandas', 'python'] = ...,\n engine : Literal['numexpr', 'python'] | None = ...,\n local_dict : dict[str, Any] | None = ...,\n global_dict: dict[str, Any] | None = ...,\n resolvers : list[Mapping[Unknown, Unknown]] | None = ...,\n level : int = ...,\n target : object | None = ...,\n inplace : Literal[True]\n) -> None: ...\ndef query(\n self : DataFrame,\n expr : str,\n *,\n inplace : Literal[False] = False,\n parser : Literal['pandas', 'python'] = ...,\n engine : Literal['numexpr', 'python'] | None = ...,\n local_dict : dict[str, Any] | None = ...,\n global_dict: dict[str, Any] | None = ...,\n resolvers : list[Mapping[Unknown, Unknown]] | None = ...,\n level : int = ...,\n target : object | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "query", "sortText": "0z.9999.query"}, {"detail": "def radd(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "radd", "sortText": "0z.9999.radd"}, {"detail": "def rank(\n self : DataFrame,\n axis : Axis = 0,\n method : Literal['average', 'dense', 'first', 'max', 'min'] = 'average',\n numeric_only: bool = False,\n na_option : Literal['bottom', 'keep', 'top'] = 'keep',\n ascending : bool = True,\n pct : bool = False\n) -> DataFrame: ...", "kind": 2, "label": "rank", "sortText": "0z.9999.rank"}, {"detail": "def rtruediv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rdiv", "sortText": "0z.9999.rdiv"}, {"detail": "def reindex(\n self : DataFrame,\n labels : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = ...,\n *,\n index : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = ...,\n columns : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n method : Literal['backfill', 'bfill', 'ffill', 'nearest', 'pad'] | None = ...,\n copy : bool = True,\n level : int | str = ...,\n fill_value: Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n limit : int | None = None,\n tolerance : Timedelta | float | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "reindex", "sortText": "0z.9999.reindex"}, {"detail": "def reindex_like(\n self : DataFrame,\n other : DataFrame,\n method : Literal['backfill', 'bfill', 'ffill', 'nearest', 'pad'] | None = ...,\n copy : bool = True,\n limit : int | None = None,\n tolerance: ExtensionArray | Index | Sequence[Scalar] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64 = ...\n) -> DataFrame: ...", "kind": 2, "label": "reindex_like", "sortText": "0z.9999.reindex_like"}, {"detail": "\n@overload\ndef rename(\n self : DataFrame,\n mapper : ((Any) -> Hashable | None) | Mapping[Any, Hashable | None] | None = ...,\n *,\n index : ((Any) -> Hashable | None) | Mapping[Any, Hashable | None] | None = ...,\n columns: ((Any) -> Hashable | None) | Mapping[Any, Hashable | None] | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n copy : bool = ...,\n inplace: Literal[True],\n level : Hashable | None = None,\n errors : IgnoreRaise = ...\n) -> None: ...\ndef rename(\n self : DataFrame,\n mapper : ((Any) -> Hashable | None) | Mapping[Any, Hashable | None] | None = ...,\n *,\n index : ((Any) -> Hashable | None) | Mapping[Any, Hashable | None] | None = ...,\n columns: ((Any) -> Hashable | None) | Mapping[Any, Hashable | None] | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n copy : bool = ...,\n inplace: Literal[False] = False,\n level : Hashable | None = None,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...", "kind": 2, "label": "rename", "sortText": "0z.9999.rename"}, {"detail": "\n@overload\ndef rename_axis(\n self : DataFrame,\n mapper : ExtensionArray | Index | SequenceNotStr[Any] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | range | str | timedelta | timedelta64 | None = ...,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n copy : bool = ...,\n inplace: Literal[True]\n) -> None: ...\ndef rename_axis(\n self : DataFrame,\n mapper : ExtensionArray | Index | SequenceNotStr[Any] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | range | str | timedelta | timedelta64 | None = ...,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n copy : bool = ...,\n inplace: Literal[False] = False\n) -> DataFrame: ...\ndef rename_axis(\n self : DataFrame,\n *,\n index : ((...) -> Unknown) | Sequence[str] | dict[int | str, str] | str | None = ...,\n columns: ((...) -> Unknown) | Sequence[str] | dict[int | str, str] | str | None = ...,\n copy : bool = ...,\n inplace: Literal[True]\n) -> None: ...\ndef rename_axis(\n self : DataFrame,\n *,\n index : ((...) -> Unknown) | Sequence[str] | dict[int | str, str] | str | None = ...,\n columns: ((...) -> Unknown) | Sequence[str] | dict[int | str, str] | str | None = ...,\n copy : bool = ...,\n inplace: Literal[False] = False\n) -> DataFrame: ...", "kind": 2, "label": "rename_axis", "sortText": "0z.9999.rename_axis"}, {"detail": "def reorder_levels(\n self : DataFrame,\n order: list[Unknown],\n axis : Axis = 0\n) -> DataFrame: ...", "kind": 2, "label": "reorder_levels", "sortText": "0z.9999.reorder_levels"}, {"detail": "\n@overload\ndef replace[HashableT2: Hashable, HashableT3: Hashable](\n self : DataFrame,\n to_replace: Mapping[HashableT2, ReplaceValue] | Mapping[Unknown, Unknown] | NAType | Pattern[Unknown] | Sequence[Pattern[Unknown] | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n value : Mapping[HashableT3, ReplaceValue] | Mapping[Unknown, Unknown] | NAType | Pattern[Unknown] | Sequence[Pattern[Unknown] | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n *,\n inplace : Literal[True],\n regex : Mapping[HashableT3, ReplaceValue] | Mapping[Unknown, Unknown] | NAType | Pattern[Unknown] | Sequence[Pattern[Unknown] | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...\n) -> None: ...\ndef replace[HashableT2: Hashable, HashableT3: Hashable](\n self : DataFrame,\n to_replace: Mapping[HashableT2, ReplaceValue] | Mapping[Unknown, Unknown] | NAType | Pattern[Unknown] | Sequence[Pattern[Unknown] | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n value : Mapping[HashableT3, ReplaceValue] | Mapping[Unknown, Unknown] | NAType | Pattern[Unknown] | Sequence[Pattern[Unknown] | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n *,\n inplace : Literal[False] = False,\n regex : Mapping[HashableT3, ReplaceValue] | Mapping[Unknown, Unknown] | NAType | Pattern[Unknown] | Sequence[Pattern[Unknown] | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "replace", "sortText": "0z.9999.replace"}, {"detail": "def resample(\n self : DataFrame,\n rule : BaseOffset | str | timedelta,\n axis : Literal['columns', 'index', 0, 1] | _NoDefault = 0,\n closed : Literal['left', 'right'] | None = None,\n label : Literal['left', 'right'] | None = None,\n on : Hashable | None = None,\n level : Hashable | None = None,\n origin : Timestamp | date | datetime | datetime64 | float | integer | str = 'start_day',\n offset : Timedelta | float | integer | str | timedelta | timedelta64 | None = None,\n group_keys: bool = False\n) -> DatetimeIndexResampler[DataFrame]: ...", "kind": 2, "label": "resample", "sortText": "0z.9999.resample"}, {"detail": "\n@overload\ndef reset_index(\n self : DataFrame,\n level : Hashable | Sequence[Hashable] = ...,\n *,\n drop : bool = ...,\n col_level : int | str = ...,\n col_fill : Hashable = ...,\n inplace : Literal[True],\n allow_duplicates: bool = ...,\n names : Hashable | Sequence[Hashable] = ...\n) -> None: ...\ndef reset_index(\n self : DataFrame,\n level : Hashable | Sequence[Hashable] = ...,\n *,\n col_level : int | str = ...,\n col_fill : Hashable = ...,\n drop : bool = ...,\n inplace : Literal[False] = False,\n allow_duplicates: bool = ...,\n names : Hashable | Sequence[Hashable] = ...\n) -> DataFrame: ...", "kind": 2, "label": "reset_index", "sortText": "0z.9999.reset_index"}, {"detail": "def rfloordiv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rfloordiv", "sortText": "0z.9999.rfloordiv"}, {"detail": "def rmod(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rmod", "sortText": "0z.9999.rmod"}, {"detail": "def rmul(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rmul", "sortText": "0z.9999.rmul"}, {"detail": "\n@overload\ndef rolling(\n self : DataFrame,\n window : BaseIndexer | BaseOffset | int | str | timedelta,\n min_periods: int | None = ...,\n center : bool = ...,\n on : Hashable | None = ...,\n axis : AxisIndex = ...,\n closed : Literal['both', 'left', 'neither', 'right'] | None = ...,\n step : int | None = ...,\n method : CalculationMethod = ...,\n *,\n win_type : str\n) -> Window[DataFrame]: ...\ndef rolling(\n self : DataFrame,\n window : BaseIndexer | BaseOffset | int | str | timedelta,\n min_periods: int | None = ...,\n center : bool = ...,\n on : Hashable | None = ...,\n axis : AxisIndex = ...,\n closed : Literal['both', 'left', 'neither', 'right'] | None = ...,\n step : int | None = ...,\n method : CalculationMethod = ...,\n *,\n win_type : None = None\n) -> Rolling[DataFrame]: ...", "kind": 2, "label": "rolling", "sortText": "0z.9999.rolling"}, {"detail": "def round(\n self : DataFrame,\n decimals: Series | dict[Unknown, Unknown] | int = ...,\n *args : Any,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "round", "sortText": "0z.9999.round"}, {"detail": "def rpow(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rpow", "sortText": "0z.9999.rpow"}, {"detail": "def rsub(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rsub", "sortText": "0z.9999.rsub"}, {"detail": "def rtruediv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rtruediv", "sortText": "0z.9999.rtruediv"}, {"detail": "def sample(\n self : DataFrame,\n n : int | None = ...,\n frac : float | None = ...,\n replace : bool = False,\n weights : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | str | None = ...,\n random_state: BitGenerator | ExtensionArray | Generator | RandomState | int | ndarray | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n ignore_index: bool = False\n) -> DataFrame: ...", "kind": 2, "label": "sample", "sortText": "0z.9999.sample"}, {"detail": "\n@overload\ndef select_dtypes(\n self : DataFrame,\n include: StrDtypeArg,\n exclude: Literal[' Never: ...\ndef select_dtypes(\n self: DataFrame,\n include: Literal[' Never: ...\ndef select_dtypes(\n self: DataFrame,\n exclude: StrDtypeArg\n) -> Never: ...\ndef select_dtypes(\n self: DataFrame,\n include: list[Never],\n exclude: list[Never]\n) -> Never: ...\ndef select_dtypes(\n self : DataFrame,\n include: _AstypeArgExtList,\n exclude: Literal[' DataFrame: ...\ndef select_dtypes(\n self: DataFrame,\n include: Literal[' DataFrame: ...\ndef select_dtypes(\n self: DataFrame,\n exclude: _AstypeArgExtList\n) -> DataFrame: ...", "kind": 2, "label": "select_dtypes", "sortText": "0z.9999.select_dtypes"}, {"detail": "def sem(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n skipna : bool | None = True,\n ddof : int = 1,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "sem", "sortText": "0z.9999.sem"}, {"detail": "def set_axis(\n self : DataFrame,\n labels: AxesData,\n *,\n axis : Axis = 0\n) -> DataFrame: ...", "kind": 2, "label": "set_axis", "sortText": "0z.9999.set_axis"}, {"detail": "def set_flags(\n self : DataFrame,\n *,\n copy : bool = ...,\n allows_duplicate_labels: bool | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "set_flags", "sortText": "0z.9999.set_flags"}, {"detail": "\n@overload\ndef set_index(\n self : DataFrame,\n keys : Hashable | Index | Iterator[Hashable] | Sequence[Hashable] | Series | ndarray | None,\n *,\n drop : bool = ...,\n append : bool = ...,\n verify_integrity: bool = ...,\n inplace : Literal[True]\n) -> None: ...\ndef set_index(\n self : DataFrame,\n keys : Hashable | Index | Iterator[Hashable] | Sequence[Hashable] | Series | ndarray | None,\n *,\n drop : bool = ...,\n append : bool = ...,\n verify_integrity: bool = ...,\n inplace : Literal[False] = False\n) -> DataFrame: ...", "kind": 2, "label": "set_index", "sortText": "0z.9999.set_index"}, {"detail": "tuple[int, int]", "kind": 5, "label": "shape", "sortText": "0z.9999.shape"}, {"detail": "def shift(\n self : DataFrame,\n periods : Sequence[int] | int = ...,\n freq : BaseOffset | str | timedelta | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n fill_value: NAType | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "shift", "sortText": "0z.9999.shift"}, {"detail": "int", "kind": 5, "label": "size", "sortText": "0z.9999.size"}, {"detail": "def skew(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "skew", "sortText": "0z.9999.skew"}, {"detail": "\n@overload\ndef sort_index(\n self : DataFrame,\n *,\n axis : Axis = 0,\n level : Hashable | None = None,\n ascending : Sequence[bool] | bool = ...,\n kind : SortKind = ...,\n na_position : NaPosition = ...,\n sort_remaining: bool = ...,\n ignore_index : bool = ...,\n inplace : Literal[True],\n key : IndexKeyFunc = ...\n) -> None: ...\ndef sort_index(\n self : DataFrame,\n *,\n axis : Axis = 0,\n level : Hashable | list[int] | list[str] | None = ...,\n ascending : Sequence[bool] | bool = ...,\n kind : SortKind = ...,\n na_position : NaPosition = ...,\n sort_remaining: bool = ...,\n ignore_index : bool = ...,\n inplace : Literal[False] = False,\n key : IndexKeyFunc = ...\n) -> DataFrame: ...", "kind": 2, "label": "sort_index", "sortText": "0z.9999.sort_index"}, {"detail": "\n@overload\ndef sort_values(\n self : DataFrame,\n by : Sequence[str] | str,\n *,\n axis : Axis = 0,\n ascending : Sequence[bool] | bool = ...,\n kind : SortKind = ...,\n na_position : NaPosition = ...,\n ignore_index: bool = ...,\n inplace : Literal[True],\n key : ValueKeyFunc = ...\n) -> None: ...\ndef sort_values(\n self : DataFrame,\n by : Sequence[str] | str,\n *,\n axis : Axis = 0,\n ascending : Sequence[bool] | bool = ...,\n kind : SortKind = ...,\n na_position : NaPosition = ...,\n ignore_index: bool = ...,\n inplace : Literal[False] = False,\n key : ValueKeyFunc = ...\n) -> DataFrame: ...", "kind": 2, "label": "sort_values", "sortText": "0z.9999.sort_values"}, {"detail": "EllipsisType", "kind": 5, "label": "sparse", "sortText": "0z.9999.sparse"}, {"detail": "def squeeze(\n self: DataFrame,\n axis: Literal['columns', 'index', 0, 1] | None = None\n) -> DataFrame | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64: ...", "kind": 2, "label": "squeeze", "sortText": "0z.9999.squeeze"}, {"detail": "\n@overload\ndef stack(\n self : DataFrame,\n level : IndexLabel = ...,\n *,\n future_stack: Literal[True]\n) -> DataFrame | Series: ...\ndef stack(\n self : DataFrame,\n level : IndexLabel = ...,\n dropna : bool = ...,\n sort : bool = ...,\n future_stack: Literal[False] = False\n) -> DataFrame | Series: ...", "kind": 2, "label": "stack", "sortText": "0z.9999.stack"}, {"detail": "def std(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = 0,\n skipna : bool = True,\n ddof : int = 1,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "std", "sortText": "0z.9999.std"}, {"detail": "Styler", "kind": 5, "label": "style", "sortText": "0z.9999.style"}, {"detail": "def sub(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "sub", "sortText": "0z.9999.sub"}, {"detail": "def sub(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "subtract", "sortText": "0z.9999.subtract"}, {"detail": "def sum(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = 0,\n skipna : bool | None = True,\n numeric_only: bool = False,\n min_count : int = 0,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "sum", "sortText": "0z.9999.sum"}, {"detail": "def swapaxes(\n self : DataFrame,\n axis1: Axis,\n axis2: Axis,\n copy : bool = ...\n) -> DataFrame: ...", "kind": 2, "label": "swapaxes", "sortText": "0z.9999.swapaxes"}, {"detail": "def swaplevel(\n self: DataFrame,\n i : Hashable = ...,\n j : Hashable = ...,\n axis: Axis = 0\n) -> DataFrame: ...", "kind": 2, "label": "swaplevel", "sortText": "0z.9999.swaplevel"}, {"detail": "def tail(\n self: DataFrame,\n n : int = 5\n) -> DataFrame: ...", "kind": 2, "label": "tail", "sortText": "0z.9999.tail"}, {"detail": "def take(\n self : DataFrame,\n indices : TakeIndexer,\n axis : Axis = 0,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "take", "sortText": "0z.9999.take"}, {"detail": "def to_clipboard[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n excel : bool = True,\n sep : str | None = None,\n *,\n na_rep : str = ...,\n float_format : ((object) -> str) | str | None = ...,\n columns : list[HashableT1] | None = ...,\n header : bool | list[str] = ...,\n index : bool = ...,\n index_label : Literal[False] | list[HashableT2] | str | None = ...,\n mode : FileWriteMode = ...,\n encoding : str | None = ...,\n compression : CompressionOptions = ...,\n quoting : CSVQuoting = ...,\n quotechar : str = ...,\n lineterminator : str | None = ...,\n chunksize : int | None = ...,\n date_format : str | None = ...,\n doublequote : bool = ...,\n escapechar : str | None = ...,\n decimal : str = ...,\n errors : str = ...,\n storage_options: StorageOptions = ...\n) -> None: ...", "kind": 2, "label": "to_clipboard", "sortText": "0z.9999.to_clipboard"}, {"detail": "\n@overload\ndef to_csv[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n path_or_buf : PathLike[str] | WriteBuffer[bytes] | WriteBuffer[str] | str,\n sep : str = ...,\n na_rep : str = ...,\n float_format : ((object) -> str) | str | None = ...,\n columns : list[HashableT1] | None = ...,\n header : bool | list[str] = ...,\n index : bool = ...,\n index_label : Literal[False] | list[HashableT2] | str | None = ...,\n mode : FileWriteMode = ...,\n encoding : str | None = ...,\n compression : CompressionOptions = ...,\n quoting : CSVQuoting = ...,\n quotechar : str = ...,\n lineterminator : str | None = ...,\n chunksize : int | None = ...,\n date_format : str | None = ...,\n doublequote : bool = ...,\n escapechar : str | None = ...,\n decimal : str = ...,\n errors : OpenFileErrors = ...,\n storage_options: StorageOptions = ...\n) -> None: ...\ndef to_csv[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n path_or_buf : None = None,\n sep : str = ...,\n na_rep : str = ...,\n float_format : ((object) -> str) | str | None = ...,\n columns : list[HashableT1] | None = ...,\n header : bool | list[str] = ...,\n index : bool = ...,\n index_label : Literal[False] | list[HashableT2] | str | None = ...,\n mode : FileWriteMode = ...,\n encoding : str | None = ...,\n compression : CompressionOptions = ...,\n quoting : CSVQuoting = ...,\n quotechar : str = ...,\n lineterminator : str | None = ...,\n chunksize : int | None = ...,\n date_format : str | None = ...,\n doublequote : bool = ...,\n escapechar : str | None = ...,\n decimal : str = ...,\n errors : OpenFileErrors = ...,\n storage_options: StorageOptions = ...\n) -> str: ...", "kind": 2, "label": "to_csv", "sortText": "0z.9999.to_csv"}, {"detail": "\n@overload\ndef to_dict(\n self : DataFrame,\n orient: str = ...,\n *,\n into : type[defaultdict[Unknown, Unknown]],\n index : Literal[True] = True\n) -> Never: ...\ndef to_dict[_T_MUTABLE_MAPPING_co: MutableMapping[Unknown, Unknown]](\n self : DataFrame,\n orient: Literal['records'],\n *,\n into : _T_MUTABLE_MAPPING_co | type[_T_MUTABLE_MAPPING_co],\n index : Literal[True] = True\n) -> list[_T_MUTABLE_MAPPING_co]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['records'],\n *,\n into : type[dict[Unknown, Unknown]] = ...,\n index : Literal[True] = True\n) -> list[dict[Hashable, Any]]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['index'],\n *,\n into : defaultdict[Unknown, Unknown],\n index : Literal[True] = True\n) -> defaultdict[Hashable, dict[Hashable, Any]]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['index'],\n *,\n into : OrderedDict[Unknown, Unknown] | type[OrderedDict[Unknown, Unknown]],\n index : Literal[True] = True\n) -> OrderedDict[Hashable, dict[Hashable, Any]]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['index'],\n *,\n into : type[MutableMapping[Unknown, Unknown]],\n index : Literal[True] = True\n) -> MutableMapping[Hashable, dict[Hashable, Any]]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['index'],\n *,\n into : type[dict[Unknown, Unknown]] = ...,\n index : Literal[True] = True\n) -> dict[Hashable, dict[Hashable, Any]]: ...\ndef to_dict[_T_MUTABLE_MAPPING_co: MutableMapping[Unknown, Unknown]](\n self : DataFrame,\n orient: Literal['dict', 'list', 'series'] = ...,\n *,\n into : _T_MUTABLE_MAPPING_co | type[_T_MUTABLE_MAPPING_co],\n index : Literal[True] = True\n) -> _T_MUTABLE_MAPPING_co: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['dict', 'list', 'series'] = ...,\n *,\n into : type[dict[Unknown, Unknown]] = ...,\n index : Literal[True] = True\n) -> dict[Hashable, Any]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['split', 'tight'],\n *,\n into : MutableMapping[Unknown, Unknown] | type[MutableMapping[Unknown, Unknown]],\n index : bool = ...\n) -> MutableMapping[str, list[Unknown]]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['split', 'tight'],\n *,\n into : type[dict[Unknown, Unknown]] = ...,\n index : bool = ...\n) -> dict[str, list[Unknown]]: ...", "kind": 2, "label": "to_dict", "sortText": "0z.9999.to_dict"}, {"detail": "def to_excel(\n self : DataFrame,\n excel_writer: Unknown,\n sheet_name : str = 'Sheet1',\n na_rep : str = '',\n float_format: str | None = ...,\n columns : Sequence[str] | str | None = ...,\n header : bool | list[str] = True,\n index : bool = True,\n index_label : Sequence[str] | str | None = ...,\n startrow : int = 0,\n startcol : int = 0,\n engine : str | None = ...,\n merge_cells : ExcelWriterMergeCells = True,\n inf_rep : str = 'inf',\n freeze_panes: tuple[int, int] | None = ...\n) -> None: ...", "kind": 2, "label": "to_excel", "sortText": "0z.9999.to_excel"}, {"detail": "def to_feather(\n self: DataFrame,\n path: PathLike[str] | WriteBuffer[bytes] | str,\n **kwargs: Any\n) -> None: ...", "kind": 2, "label": "to_feather", "sortText": "0z.9999.to_feather"}, {"detail": "def to_hdf[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n path_or_buf : HDFStore | PathLike[str] | str,\n *,\n key : str,\n mode : Literal['a', 'r+', 'w'] = ...,\n complevel : int | None = ...,\n complib : Literal['blosc', 'bzip2', 'lzo', 'zlib'] | None = ...,\n append : bool = ...,\n format : Literal['f', 'fixed', 't', 'table'] | None = ...,\n index : bool = ...,\n min_itemsize: dict[HashableT1, int] | int | None = ...,\n nan_rep : str | None = ...,\n dropna : bool | None = ...,\n data_columns: Literal[True] | list[HashableT2] | None = ...,\n errors : OpenFileErrors = ...,\n encoding : str = ...\n) -> None: ...", "kind": 2, "label": "to_hdf", "sortText": "0z.9999.to_hdf"}, {"detail": "\n@overload\ndef to_html(\n self : DataFrame,\n buf : PathLike[str] | WriteBuffer[str] | str,\n *,\n columns : Index | SequenceNotStr[Hashable] | Series | None = ...,\n col_space : Mapping[Hashable, int | str] | Sequence[int | str] | int | str | None = ...,\n header : bool = ...,\n index : bool = ...,\n na_rep : str = ...,\n formatters : Mapping[Hashable, (object) -> str] | list[(object) -> str] | tuple[(object) -> str, ...] | None = ...,\n float_format : ((float) -> str) | None = ...,\n sparsify : bool | None = ...,\n index_names : bool = ...,\n justify : Literal['center', 'end', 'inherit', 'initial', 'justify', 'justify-all', 'left', 'match-parent', 'right', 'start', 'unset'] | None = ...,\n max_rows : int | None = ...,\n max_cols : int | None = ...,\n show_dimensions: bool = ...,\n decimal : str = ...,\n bold_rows : bool = ...,\n classes : Sequence[str] | None = ...,\n escape : bool = ...,\n notebook : bool = ...,\n border : int | None = ...,\n table_id : str | None = ...,\n render_links : bool = ...,\n encoding : str | None = ...\n) -> None: ...\ndef to_html(\n self : DataFrame,\n buf : None = None,\n *,\n columns : Sequence[Hashable] | None = ...,\n col_space : Mapping[Hashable, int | str] | Sequence[int | str] | int | str | None = ...,\n header : bool = ...,\n index : bool = ...,\n na_rep : str = ...,\n formatters : Mapping[Hashable, (object) -> str] | list[(object) -> str] | tuple[(object) -> str, ...] | None = ...,\n float_format : ((float) -> str) | None = ...,\n sparsify : bool | None = ...,\n index_names : bool = ...,\n justify : Literal['center', 'end', 'inherit', 'initial', 'justify', 'justify-all', 'left', 'match-parent', 'right', 'start', 'unset'] | None = ...,\n max_rows : int | None = ...,\n max_cols : int | None = ...,\n show_dimensions: bool = ...,\n decimal : str = ...,\n bold_rows : bool = ...,\n classes : Sequence[str] | None = ...,\n escape : bool = ...,\n notebook : bool = ...,\n border : int | None = ...,\n table_id : str | None = ...,\n render_links : bool = ...,\n encoding : str | None = ...\n) -> str: ...", "kind": 2, "label": "to_html", "sortText": "0z.9999.to_html"}, {"detail": "\n@overload\ndef to_json(\n self : DataFrame,\n path_or_buf : PathLike[str] | WriteBuffer[str] | str,\n *,\n orient : Literal['records'],\n date_format : Literal['epoch', 'iso'] | None = ...,\n double_precision: int = ...,\n force_ascii : bool = ...,\n date_unit : TimeUnit = ...,\n default_handler : ((Any) -> JSONSerializable) | None = ...,\n lines : Literal[True],\n compression : CompressionOptions = ...,\n index : bool | None = ...,\n indent : int | None = ...,\n storage_options : dict[Unknown, Unknown] | None = ...,\n mode : Literal['a']\n) -> None: ...\ndef to_json(\n self : DataFrame,\n path_or_buf : None = None,\n *,\n orient : Literal['records'],\n date_format : Literal['epoch', 'iso'] | None = ...,\n double_precision: int = ...,\n force_ascii : bool = ...,\n date_unit : TimeUnit = ...,\n default_handler : ((Any) -> JSONSerializable) | None = ...,\n lines : Literal[True],\n compression : CompressionOptions = ...,\n index : bool | None = ...,\n indent : int | None = ...,\n storage_options : dict[Unknown, Unknown] | None = ...,\n mode : Literal['a']\n) -> str: ...\ndef to_json(\n self : DataFrame,\n path_or_buf : None = None,\n *,\n orient : Literal['columns', 'index', 'records', 'split', 'table', 'values'] | None = ...,\n date_format : Literal['epoch', 'iso'] | None = ...,\n double_precision: int = ...,\n force_ascii : bool = ...,\n date_unit : TimeUnit = ...,\n default_handler : ((Any) -> JSONSerializable) | None = ...,\n lines : bool = ...,\n compression : CompressionOptions = ...,\n index : bool | None = ...,\n indent : int | None = ...,\n storage_options : dict[Unknown, Unknown] | None = ...,\n mode : Literal['w'] = ...\n) -> str: ...\ndef to_json(\n self : DataFrame,\n path_or_buf : PathLike[str] | WriteBuffer[bytes] | WriteBuffer[str] | str,\n *,\n orient : Literal['columns', 'index', 'records', 'split', 'table', 'values'] | None = ...,\n date_format : Literal['epoch', 'iso'] | None = ...,\n double_precision: int = ...,\n force_ascii : bool = ...,\n date_unit : TimeUnit = ...,\n default_handler : ((Any) -> JSONSerializable) | None = ...,\n lines : bool = ...,\n compression : CompressionOptions = ...,\n index : bool | None = ...,\n indent : int | None = ...,\n storage_options : dict[Unknown, Unknown] | None = ...,\n mode : Literal['w'] = ...\n) -> None: ...", "kind": 2, "label": "to_json", "sortText": "0z.9999.to_json"}, {"detail": "\n@overload\ndef to_latex(\n self : DataFrame,\n buf : PathLike[str] | WriteBuffer[str] | str,\n columns : list[str] | None = ...,\n header : bool | list[str] = ...,\n index : bool = ...,\n na_rep : str = ...,\n formatters : Unknown = ...,\n float_format : Unknown = ...,\n sparsify : bool | None = ...,\n index_names : bool = ...,\n bold_rows : bool = ...,\n column_format : str | None = ...,\n longtable : bool | None = ...,\n escape : bool | None = ...,\n encoding : str | None = ...,\n decimal : str = ...,\n multicolumn : bool | None = ...,\n multicolumn_format: str | None = ...,\n multirow : bool | None = ...,\n caption : str | tuple[str, str] | None = ...,\n label : str | None = ...,\n position : str | None = ...\n) -> None: ...\ndef to_latex(\n self : DataFrame,\n buf : None = None,\n columns : list[str] | None = ...,\n header : bool | list[str] = ...,\n index : bool = ...,\n na_rep : str = ...,\n formatters : Unknown = ...,\n float_format : Unknown = ...,\n sparsify : bool | None = ...,\n index_names : bool = ...,\n bold_rows : bool = ...,\n column_format : str | None = ...,\n longtable : bool | None = ...,\n escape : bool | None = ...,\n encoding : str | None = ...,\n decimal : str = ...,\n multicolumn : bool | None = ...,\n multicolumn_format: str | None = ...,\n multirow : bool | None = ...,\n caption : str | tuple[str, str] | None = ...,\n label : str | None = ...,\n position : str | None = ...\n) -> str: ...", "kind": 2, "label": "to_latex", "sortText": "0z.9999.to_latex"}, {"detail": "\n@overload\ndef to_markdown(\n self : DataFrame,\n buf : PathLike[str] | WriteBuffer[str] | str,\n *,\n mode : FileWriteMode = ...,\n index : bool = ...,\n storage_options: StorageOptions = ...,\n **kwargs : Any\n) -> None: ...\ndef to_markdown(\n self : DataFrame,\n buf : None = None,\n *,\n mode : Literal['a', 'a+', 'a+b', 'ab', 'at', 'w', 'w+', 'w+b', 'wb', 'wt', 'x', 'xb', 'xt'] | None = ...,\n index : bool = ...,\n storage_options: StorageOptions = ...,\n **kwargs : Any\n) -> str: ...", "kind": 2, "label": "to_markdown", "sortText": "0z.9999.to_markdown"}, {"detail": "def to_numpy(\n self : DataFrame,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = ...,\n copy : bool = False,\n na_value: Scalar = ...\n) -> ndarray[tuple[int, int]]: ...", "kind": 2, "label": "to_numpy", "sortText": "0z.9999.to_numpy"}, {"detail": "\n@overload\ndef to_orc(\n self : DataFrame,\n path : PathLike[str] | WriteBuffer[bytes] | str,\n *,\n engine : Literal['pyarrow'] = ...,\n index : bool | None = ...,\n engine_kwargs: dict[str, Any] | None = ...\n) -> None: ...\ndef to_orc(\n self : DataFrame,\n path : None = None,\n *,\n engine : Literal['pyarrow'] = ...,\n index : bool | None = ...,\n engine_kwargs: dict[str, Any] | None = ...\n) -> bytes: ...", "kind": 2, "label": "to_orc", "sortText": "0z.9999.to_orc"}, {"detail": "\n@overload\ndef to_parquet(\n self : DataFrame,\n path : PathLike[str] | WriteBuffer[bytes] | str,\n *,\n engine : ParquetEngine = ...,\n compression : Literal['brotli', 'gzip', 'lz4', 'snappy', 'zstd'] | None = ...,\n index : bool | None = ...,\n partition_cols : Sequence[Hashable] | None = ...,\n storage_options: StorageOptions = ...,\n **kwargs : Any\n) -> None: ...\ndef to_parquet(\n self : DataFrame,\n path : None = None,\n *,\n engine : ParquetEngine = ...,\n compression : Literal['brotli', 'gzip', 'lz4', 'snappy', 'zstd'] | None = ...,\n index : bool | None = ...,\n partition_cols : Sequence[Hashable] | None = ...,\n storage_options: StorageOptions = ...,\n **kwargs : Any\n) -> bytes: ...", "kind": 2, "label": "to_parquet", "sortText": "0z.9999.to_parquet"}, {"detail": "def to_period(\n self: DataFrame,\n freq: Day | Hour | Micro | Milli | Minute | MonthEnd | Nano | QuarterEnd | Second | Week | YearEnd | str | None = None,\n axis: Axis = 0,\n copy: bool = True\n) -> DataFrame: ...", "kind": 2, "label": "to_period", "sortText": "0z.9999.to_period"}, {"detail": "def to_pickle(\n self : DataFrame,\n path : PathLike[str] | WriteBuffer[bytes] | str,\n compression : CompressionOptions = 'infer',\n protocol : int = 5,\n storage_options: StorageOptions = ...\n) -> None: ...", "kind": 2, "label": "to_pickle", "sortText": "0z.9999.to_pickle"}, {"detail": "def to_records[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n index : bool = True,\n column_dtypes: Mapping[HashableT1, DTypeLike] | _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n index_dtypes : Mapping[HashableT2, DTypeLike] | _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None\n) -> recarray: ...", "kind": 2, "label": "to_records", "sortText": "0z.9999.to_records"}, {"detail": "def to_sql(\n self: DataFrame,\n name: str,\n con: Unknown,\n *,\n schema: str | None = None,\n if_exists: Literal['append', 'delete_rows', 'fail', 'replace'] = 'fail',\n index: bool = True,\n index_label: IndexLabel = None,\n chunksize: int | None = None,\n dtype: ExtensionDtype | Mapping[Hashable, ExtensionDtype | dtype[generic] | str | type[bool | complex | object | str]] | dtype[generic] | str | type[bool | complex | object | str] | None = None,\n method: Literal['multi'] | ((\n SQLTable,\n Any,\n list[str],\n Iterable[tuple[Any, ...]]\n) -> int | None) | None = None\n) -> int | None: ...", "kind": 2, "label": "to_sql", "sortText": "0z.9999.to_sql"}, {"detail": "\n@overload\ndef to_stata[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n path : PathLike[str] | WriteBuffer[bytes] | str,\n *,\n convert_dates : dict[HashableT1, StataDateFormat] | None = ...,\n write_index : bool = ...,\n byteorder : Literal['<', '>', 'big', 'little'] | None = ...,\n time_stamp : datetime | None = ...,\n data_label : str | None = ...,\n variable_labels: dict[HashableT2, str] | None = ...,\n version : Literal[117, 118, 119],\n convert_strl : SequenceNotStr[Hashable] | None = ...,\n compression : CompressionOptions = ...,\n storage_options: StorageOptions = ...,\n value_labels : dict[Hashable, dict[float, str]] | None = ...\n) -> None: ...\ndef to_stata[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n path : PathLike[str] | WriteBuffer[bytes] | str,\n *,\n convert_dates : dict[HashableT1, StataDateFormat] | None = ...,\n write_index : bool = ...,\n byteorder : Literal['<', '>', 'big', 'little'] | None = ...,\n time_stamp : datetime | None = ...,\n data_label : str | None = ...,\n variable_labels: dict[HashableT2, str] | None = ...,\n version : Literal[114, 117, 118, 119] | None = ...,\n convert_strl : None = None,\n compression : CompressionOptions = ...,\n storage_options: StorageOptions = ...,\n value_labels : dict[Hashable, dict[float, str]] | None = ...\n) -> None: ...", "kind": 2, "label": "to_stata", "sortText": "0z.9999.to_stata"}, {"detail": "\n@overload\ndef to_string[HashableT: Hashable](\n self : DataFrame,\n buf : PathLike[str] | WriteBuffer[str] | str,\n *,\n columns : Index | SequenceNotStr[Hashable] | Series | None = ...,\n col_space : dict[HashableT, int] | int | list[int] | None = ...,\n header : bool | list[str] | tuple[str, ...] = ...,\n index : bool = ...,\n na_rep : str = ...,\n formatters : Mapping[int | str, (...) -> Unknown] | list[(...) -> Unknown] | tuple[(...) -> Unknown, ...] | None = ...,\n float_format : ((float) -> str) | EngFormatter | str | None = ...,\n sparsify : bool | None = ...,\n index_names : bool = ...,\n justify : str | None = ...,\n max_rows : int | None = ...,\n max_cols : int | None = ...,\n show_dimensions: bool = ...,\n decimal : str = ...,\n line_width : int | None = ...,\n min_rows : int | None = ...,\n max_colwidth : int | None = ...,\n encoding : str | None = ...\n) -> None: ...\ndef to_string(\n self : DataFrame,\n buf : None = None,\n *,\n columns : Index | Sequence[Hashable] | Series | None = ...,\n col_space : dict[Hashable, int] | int | list[int] | None = ...,\n header : Sequence[str] | bool = ...,\n index : bool = ...,\n na_rep : str = ...,\n formatters : Mapping[int | str, (...) -> Unknown] | list[(...) -> Unknown] | tuple[(...) -> Unknown, ...] | None = ...,\n float_format : ((float) -> str) | EngFormatter | str | None = ...,\n sparsify : bool | None = ...,\n index_names : bool = ...,\n justify : str | None = ...,\n max_rows : int | None = ...,\n max_cols : int | None = ...,\n show_dimensions: bool = ...,\n decimal : str = ...,\n line_width : int | None = ...,\n min_rows : int | None = ...,\n max_colwidth : int | None = ...,\n encoding : str | None = ...\n) -> str: ...", "kind": 2, "label": "to_string", "sortText": "0z.9999.to_string"}, {"detail": "def to_timestamp(\n self: DataFrame,\n freq: Day | Hour | Micro | Milli | Minute | MonthEnd | Nano | QuarterEnd | Second | Week | YearEnd | str | None = None,\n how : ToTimestampHow = ...,\n axis: Axis = 0,\n copy: bool = True\n) -> DataFrame: ...", "kind": 2, "label": "to_timestamp", "sortText": "0z.9999.to_timestamp"}, {"detail": "def to_xarray(self: DataFrame) -> Unknown: ...", "kind": 2, "label": "to_xarray", "sortText": "0z.9999.to_xarray"}, {"detail": "\n@overload\ndef to_xml(\n self : DataFrame,\n path_or_buffer : PathLike[str] | WriteBuffer[bytes] | WriteBuffer[str] | str,\n index : bool = ...,\n root_name : str = ...,\n row_name : str = ...,\n na_rep : str | None = ...,\n attr_cols : SequenceNotStr[Hashable] | None = ...,\n elem_cols : SequenceNotStr[Hashable] | None = ...,\n namespaces : dict[str | None, str] | None = ...,\n prefix : str | None = ...,\n encoding : str = ...,\n xml_declaration: bool = ...,\n pretty_print : bool = ...,\n parser : XMLParsers = ...,\n stylesheet : PathLike[str] | ReadBuffer[bytes] | ReadBuffer[str] | str | None = ...,\n compression : CompressionOptions = ...,\n storage_options: StorageOptions = ...\n) -> None: ...\ndef to_xml(\n self : DataFrame,\n path_or_buffer : None = None,\n index : bool = ...,\n root_name : str | None = ...,\n row_name : str | None = ...,\n na_rep : str | None = ...,\n attr_cols : list[Hashable] | None = ...,\n elem_cols : list[Hashable] | None = ...,\n namespaces : dict[str | None, str] | None = ...,\n prefix : str | None = ...,\n encoding : str = ...,\n xml_declaration: bool | None = ...,\n pretty_print : bool | None = ...,\n parser : str | None = ...,\n stylesheet : PathLike[str] | ReadBuffer[bytes] | ReadBuffer[str] | str | None = ...,\n compression : CompressionOptions = ...,\n storage_options: StorageOptions = ...\n) -> str: ...", "kind": 2, "label": "to_xml", "sortText": "0z.9999.to_xml"}, {"detail": "def transform(\n self : DataFrame,\n func : AggFuncTypeFrame,\n axis : Axis = 0,\n *args : Any,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "transform", "sortText": "0z.9999.transform"}, {"detail": "def transpose(\n self : DataFrame,\n *args: Any,\n *,\n copy : bool = ...\n) -> DataFrame: ...", "kind": 2, "label": "transpose", "sortText": "0z.9999.transpose"}, {"detail": "def truediv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "truediv", "sortText": "0z.9999.truediv"}, {"detail": "def truncate(\n self : DataFrame,\n before: date | int | str | None = ...,\n after : date | int | str | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n copy : bool = ...\n) -> DataFrame: ...", "kind": 2, "label": "truncate", "sortText": "0z.9999.truncate"}, {"detail": "def tz_convert(\n self : DataFrame,\n tz : TimeZones,\n axis : Axis = 0,\n level: Hashable | None = None,\n copy : bool = True\n) -> DataFrame: ...", "kind": 2, "label": "tz_convert", "sortText": "0z.9999.tz_convert"}, {"detail": "def tz_localize(\n self : DataFrame,\n tz : TimeZones,\n axis : Axis = 0,\n level : Hashable | None = None,\n copy : builtins.bool = True,\n ambiguous : TimeAmbiguous = 'raise',\n nonexistent: TimeNonexistent = 'raise'\n) -> DataFrame: ...", "kind": 2, "label": "tz_localize", "sortText": "0z.9999.tz_localize"}, {"detail": "def unstack(\n self : DataFrame,\n level : IndexLabel = -1,\n fill_value: Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = None,\n sort : bool = True\n) -> DataFrame | Series: ...", "kind": 2, "label": "unstack", "sortText": "0z.9999.unstack"}, {"detail": "def update(\n self : DataFrame,\n other : DataFrame | Series,\n join : Literal['left'] = 'left',\n overwrite : bool = True,\n filter_func: ((...) -> Unknown) | None = ...,\n errors : IgnoreRaise = 'ignore'\n) -> None: ...", "kind": 2, "label": "update", "sortText": "0z.9999.update"}, {"detail": "\n@overload\ndef value_counts(\n self : DataFrame,\n subset : Sequence[Hashable] | None = ...,\n normalize: Literal[False] = False,\n sort : bool = ...,\n ascending: bool = ...,\n dropna : bool = ...\n) -> Series[int]: ...\ndef value_counts(\n self : DataFrame,\n normalize: Literal[True],\n subset : Sequence[Hashable] | None = ...,\n sort : bool = ...,\n ascending: bool = ...,\n dropna : bool = ...\n) -> Series[float]: ...", "kind": 2, "label": "value_counts", "sortText": "0z.9999.value_counts"}, {"detail": "ndarray[tuple[int, int]]", "kind": 5, "label": "values", "sortText": "0z.9999.values"}, {"detail": "def var(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = 0,\n skipna : bool | None = True,\n ddof : int = 1,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "var", "sortText": "0z.9999.var"}, {"detail": "\n@overload\ndef where(\n self : DataFrame,\n cond : ((DataFrame) -> DataFrame) | ((Any) -> builtins.bool) | DataFrame | Series | ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other : ((...) -> DataFrame | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64) | DataFrame | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 = ...,\n *,\n inplace: Literal[True],\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n level : Hashable | None = None\n) -> None: ...\ndef where(\n self : DataFrame,\n cond : ((DataFrame) -> DataFrame) | ((Any) -> builtins.bool) | DataFrame | Series | ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other : ((...) -> DataFrame | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64) | DataFrame | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 = ...,\n *,\n inplace: Literal[False] = False,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n level : Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "where", "sortText": "0z.9999.where"}, {"detail": "def xs(\n self : DataFrame,\n key : IndexLabel,\n axis : Axis = 0,\n level : Hashable | Sequence[Hashable] | None = None,\n drop_level: bool = True\n) -> DataFrame | Series: ...", "kind": 2, "label": "xs", "sortText": "0z.9999.xs"}, {"detail": "def __abs__(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "__abs__", "sortText": "3z.9999.__abs__"}, {"detail": "def __add__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__add__", "sortText": "3z.9999.__add__"}, {"detail": "def __and__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__and__", "sortText": "3z.9999.__and__"}, {"detail": "def __array__(\n self : DataFrame,\n dtype: Unknown = ...\n) -> ndarray: ...", "kind": 2, "label": "__array__", "sortText": "3z.9999.__array__"}, {"detail": "int", "kind": 5, "label": "__array_priority__", "sortText": "3z.9999.__array_priority__"}, {"detail": "def __arrow_c_stream__(\n self : DataFrame,\n requested_schema: object | None = None\n) -> object: ...", "kind": 2, "label": "__arrow_c_stream__", "sortText": "3z.9999.__arrow_c_stream__"}, {"detail": "def __bool__(self: DataFrame) -> NoReturn: ...", "kind": 2, "label": "__bool__", "sortText": "3z.9999.__bool__"}, {"detail": "def __contains__(\n self: DataFrame,\n key: Unknown\n) -> bool: ...", "kind": 2, "label": "__contains__", "sortText": "3z.9999.__contains__"}, {"detail": "def __copy__(\n self: DataFrame,\n deep: bool = ...\n) -> DataFrame: ...", "kind": 2, "label": "__copy__", "sortText": "3z.9999.__copy__"}, {"detail": "def __deepcopy__(\n self: DataFrame,\n memo: MutableMapping[int, Any] | None = None\n) -> DataFrame: ...", "kind": 2, "label": "__deepcopy__", "sortText": "3z.9999.__deepcopy__"}, {"detail": "def __delitem__(\n self: DataFrame,\n idx: Hashable\n) -> None: ...", "kind": 2, "label": "__delitem__", "sortText": "3z.9999.__delitem__"}, {"detail": "def __divmod__(\n self: DataFrame,\n other: Any\n) -> tuple[DataFrame, DataFrame]: ...", "kind": 2, "label": "__divmod__", "sortText": "3z.9999.__divmod__"}, {"detail": "def __eq__(\n self: DataFrame,\n other: object\n) -> DataFrame: ...", "kind": 2, "label": "__eq__", "sortText": "3z.9999.__eq__"}, {"detail": "def __finalize__(\n self : DataFrame,\n other : Unknown,\n method : Unknown = ...,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "__finalize__", "sortText": "3z.9999.__finalize__"}, {"detail": "def __floordiv__(\n self: DataFrame,\n other: DataFrame | Sequence[float] | Series[float] | Series[int] | float\n) -> DataFrame: ...", "kind": 2, "label": "__floordiv__", "sortText": "3z.9999.__floordiv__"}, {"detail": "def __ge__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__ge__", "sortText": "3z.9999.__ge__"}, {"detail": "def __getattr__(\n self: DataFrame,\n name: str\n) -> Series: ...", "kind": 2, "label": "__getattr__", "sortText": "3z.9999.__getattr__"}, {"detail": "\n@overload\ndef __getitem__(\n self: DataFrame,\n key: Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | tuple[Hashable, ...]\n) -> Series: ...\ndef __getitem__(\n self: DataFrame,\n key: Iterable[Hashable] | slice[Any, Any, Any]\n) -> DataFrame: ...\ndef __getitem__(\n self: DataFrame,\n key: Hashable\n) -> Series: ...", "kind": 2, "label": "__getitem__", "sortText": "3z.9999.__getitem__"}, {"detail": "def __gt__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__gt__", "sortText": "3z.9999.__gt__"}, {"detail": "None", "kind": 5, "label": "__hash__", "sortText": "3z.9999.__hash__"}, {"detail": "def __invert__(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "__invert__", "sortText": "3z.9999.__invert__"}, {"detail": "def __iter__(self: DataFrame) -> Iterator[Hashable]: ...", "kind": 2, "label": "__iter__", "sortText": "3z.9999.__iter__"}, {"detail": "def __le__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__le__", "sortText": "3z.9999.__le__"}, {"detail": "def __len__(self: DataFrame) -> int: ...", "kind": 2, "label": "__len__", "sortText": "3z.9999.__len__"}, {"detail": "def __lt__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__lt__", "sortText": "3z.9999.__lt__"}, {"detail": "\n@overload\ndef __matmul__(\n self: DataFrame,\n other: DataFrame\n) -> DataFrame: ...\ndef __matmul__(\n self: DataFrame,\n other: Series\n) -> Series: ...\ndef __matmul__(\n self: DataFrame,\n other: ndarray[tuple[Any, ...], dtype[np_num]]\n) -> DataFrame: ...", "kind": 2, "label": "__matmul__", "sortText": "3z.9999.__matmul__"}, {"detail": "def __mod__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__mod__", "sortText": "3z.9999.__mod__"}, {"detail": "def __mul__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__mul__", "sortText": "3z.9999.__mul__"}, {"detail": "def __ne__(\n self: DataFrame,\n other: object\n) -> DataFrame: ...", "kind": 2, "label": "__ne__", "sortText": "3z.9999.__ne__"}, {"detail": "def __neg__(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "__neg__", "sortText": "3z.9999.__neg__"}, {"detail": "\n@overload\ndef __new__[Self@DataFrame: DataFrame](\n cls : type[Self@DataFrame],\n data : DataFrame | Index | Iterable[Index | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | tuple[Hashable, ListLikeU]] | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | None = None,\n index : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n columns: ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n dtype : ExtensionDtype | dtype[generic] | str | type[bool | complex | object | str] | None = None,\n copy : bool | None = None\n) -> Self@DataFrame: ...\ndef __new__[Self@DataFrame: DataFrame](\n cls : type[Self@DataFrame],\n data : Scalar,\n index : Axes,\n columns: Axes,\n dtype : ExtensionDtype | dtype[generic] | str | type[bool | complex | object | str] | None = None,\n copy : bool | None = None\n) -> Self@DataFrame: ...", "kind": 3, "label": "__new__", "sortText": "3z.9999.__new__"}, {"detail": "def __nonzero__(self: DataFrame) -> None: ...", "kind": 2, "label": "__nonzero__", "sortText": "3z.9999.__nonzero__"}, {"detail": "def __or__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__or__", "sortText": "3z.9999.__or__"}, {"detail": "def __pos__(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "__pos__", "sortText": "3z.9999.__pos__"}, {"detail": "def __pow__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__pow__", "sortText": "3z.9999.__pow__"}, {"detail": "def __radd__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__radd__", "sortText": "3z.9999.__radd__"}, {"detail": "def __rand__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__rand__", "sortText": "3z.9999.__rand__"}, {"detail": "def __rdivmod__(\n self: DataFrame,\n other: Any\n) -> tuple[DataFrame, DataFrame]: ...", "kind": 2, "label": "__rdivmod__", "sortText": "3z.9999.__rdivmod__"}, {"detail": "def __rfloordiv__(\n self: DataFrame,\n other: DataFrame | Sequence[float] | Series[float] | Series[int] | float\n) -> DataFrame: ...", "kind": 2, "label": "__rfloordiv__", "sortText": "3z.9999.__rfloordiv__"}, {"detail": "def __rmatmul__(\n self: DataFrame,\n other: ndarray[tuple[Any, ...], dtype[np_num]]\n) -> DataFrame: ...", "kind": 2, "label": "__rmatmul__", "sortText": "3z.9999.__rmatmul__"}, {"detail": "def __rmod__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__rmod__", "sortText": "3z.9999.__rmod__"}, {"detail": "def __rmul__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__rmul__", "sortText": "3z.9999.__rmul__"}, {"detail": "def __ror__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__ror__", "sortText": "3z.9999.__ror__"}, {"detail": "def __round__(\n self : DataFrame,\n decimals: int = ...\n) -> DataFrame: ...", "kind": 2, "label": "__round__", "sortText": "3z.9999.__round__"}, {"detail": "def __rpow__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__rpow__", "sortText": "3z.9999.__rpow__"}, {"detail": "def __rsub__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__rsub__", "sortText": "3z.9999.__rsub__"}, {"detail": "def __rtruediv__(\n self: DataFrame,\n other: DataFrame | Sequence[Unknown] | Series | float\n) -> DataFrame: ...", "kind": 2, "label": "__rtruediv__", "sortText": "3z.9999.__rtruediv__"}, {"detail": "def __rxor__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__rxor__", "sortText": "3z.9999.__rxor__"}, {"detail": "def __setattr__(\n self: DataFrame,\n name: str,\n value: Unknown\n) -> None: ...", "kind": 2, "label": "__setattr__", "sortText": "3z.9999.__setattr__"}, {"detail": "\n@overload\ndef __setitem__(\n self: DataFrame,\n idx: Index | Series[int] | int | list[int] | ndarray[tuple[Any, ...], dtype[integer]] | slice[Any, Any, Any] | tuple[IndexType, IndexType] | tuple[IndexType, int] | tuple[int, IndexType] | tuple[int, int],\n value: DataFrame | IndexOpsMixin | Mapping[Hashable, NAType | NaTType | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | NAType | NaTType | Sequence[Scalar] | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64 | None\n) -> None: ...\ndef __setitem__[ScalarT: Scalar](\n self: DataFrame,\n idx: Series[builtins.bool] | int | integer | list[builtins.bool] | list[ScalarT] | ndarray[tuple[Any, ...], dtype[numpy.bool]] | signedinteger[_NBitIntP] | signedinteger[_8Bit] | signedinteger | slice[Any, Any, Any] | str | str_ | unsignedinteger | tuple[IndexOpsMixin | Sequence[Scalar] | Series[builtins.bool] | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | list[builtins.bool] | ndarray[tuple[Any, ...], dtype[numpy.bool]] | slice[Any, Any, Any] | str | timedelta | timedelta64, ...],\n value: DataFrame | ExtensionArray | IndexOpsMixin | Mapping[Hashable, NAType | NaTType | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | NAType | NaTType | Sequence[Scalar] | Sequence[Sequence[Scalar]] | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64 | None\n) -> None: ...\ndef __setitem__(\n self: DataFrame,\n idx: tuple[tuple[IndexOpsMixin | Sequence[Scalar] | Series[builtins.bool] | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | list[builtins.bool] | ndarray[tuple[Any, ...], dtype[numpy.bool]] | slice[Any, Any, Any] | str | timedelta | timedelta64, ...], Hashable],\n value: ExtensionArray | IndexOpsMixin | Mapping[Hashable, NAType | NaTType | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | NAType | NaTType | Sequence[Scalar] | Sequence[Sequence[Scalar]] | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64 | None\n) -> None: ...\ndef __setitem__(\n self: DataFrame,\n idx: DataFrame | IndexOpsMixin,\n value: ExtensionArray | IndexOpsMixin | Mapping[Hashable, NAType | NaTType | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | NAType | NaTType | Sequence[Scalar] | Sequence[Sequence[Scalar]] | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64 | None\n) -> None: ...", "kind": 2, "label": "__setitem__", "sortText": "3z.9999.__setitem__"}, {"detail": "def __sub__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__sub__", "sortText": "3z.9999.__sub__"}, {"detail": "def __truediv__(\n self: DataFrame,\n other: DataFrame | Sequence[Unknown] | Series | float\n) -> DataFrame: ...", "kind": 2, "label": "__truediv__", "sortText": "3z.9999.__truediv__"}, {"detail": "def __xor__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__xor__", "sortText": "3z.9999.__xor__"}]}} {"suite": "pandas", "label": "edit dataframe then complete (edit+completion)", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 17, "character": 39, "iteration": 5, "result": {"isIncomplete": false, "items": [{"detail": "str", "kind": 5, "label": "Name", "sortText": "0.9999.Name"}, {"detail": "DataFrame", "kind": 5, "label": "T", "sortText": "0z.9999.T"}, {"detail": "def abs(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "abs", "sortText": "0z.9999.abs"}, {"detail": "def add(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "add", "sortText": "0z.9999.add"}, {"detail": "def add_prefix(\n self : DataFrame,\n prefix: str,\n axis : Literal['columns', 'index', 0, 1] | None = None\n) -> DataFrame: ...", "kind": 2, "label": "add_prefix", "sortText": "0z.9999.add_prefix"}, {"detail": "def add_suffix(\n self : DataFrame,\n suffix: str,\n axis : Literal['columns', 'index', 0, 1] | None = None\n) -> DataFrame: ...", "kind": 2, "label": "add_suffix", "sortText": "0z.9999.add_suffix"}, {"detail": "\n@overload\ndef aggregate(\n self : DataFrame,\n func : ((...) -> Unknown) | Mapping[Unknown, ((...) -> Unknown) | str | ufunc] | str | ufunc,\n axis : Axis = 0,\n **kwargs: Any\n) -> Series: ...\ndef aggregate(\n self : DataFrame,\n func : Mapping[Unknown, ((...) -> Unknown) | list[((...) -> Unknown) | str | ufunc] | str | ufunc] | list[AggFuncTypeBase] | None = ...,\n axis : Axis = 0,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "agg", "sortText": "0z.9999.agg"}, {"detail": "\n@overload\ndef aggregate(\n self : DataFrame,\n func : ((...) -> Unknown) | Mapping[Unknown, ((...) -> Unknown) | str | ufunc] | str | ufunc,\n axis : Axis = 0,\n **kwargs: Any\n) -> Series: ...\ndef aggregate(\n self : DataFrame,\n func : Mapping[Unknown, ((...) -> Unknown) | list[((...) -> Unknown) | str | ufunc] | str | ufunc] | list[AggFuncTypeBase] | None = ...,\n axis : Axis = 0,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "aggregate", "sortText": "0z.9999.aggregate"}, {"detail": "def align[NDFrameT: NDFrame](\n self : DataFrame,\n other : NDFrameT,\n join : AlignJoin = 'outer',\n axis : Literal['columns', 'index', 0, 1] | None = None,\n level : Hashable | None = None,\n copy : bool = True,\n fill_value: NAType | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...\n) -> tuple[DataFrame, NDFrameT]: ...", "kind": 2, "label": "align", "sortText": "0z.9999.align"}, {"detail": "\n@overload\ndef all(\n self : DataFrame,\n axis : None,\n bool_only: builtins.bool | None = ...,\n skipna : builtins.bool = ...,\n **kwargs : Any\n) -> numpy.bool: ...\ndef all(\n self : DataFrame,\n axis : Axis = 0,\n bool_only: builtins.bool | None = ...,\n skipna : builtins.bool = ...,\n **kwargs : Any\n) -> Series[builtins.bool]: ...", "kind": 2, "label": "all", "sortText": "0z.9999.all"}, {"detail": "\n@overload\ndef any(\n self : DataFrame,\n *,\n axis : None,\n bool_only: builtins.bool | None = ...,\n skipna : builtins.bool = ...,\n **kwargs : Any\n) -> numpy.bool: ...\ndef any(\n self : DataFrame,\n *,\n axis : Axis = 0,\n bool_only: builtins.bool | None = ...,\n skipna : builtins.bool = ...,\n **kwargs : Any\n) -> Series[builtins.bool]: ...", "kind": 2, "label": "any", "sortText": "0z.9999.any"}, {"detail": "\n@overload\ndef apply(\n self : DataFrame,\n f : (...) -> Index | MutableSequence[Any] | Series | ndarray[tuple[int]] | tuple[Any, ...],\n axis : AxisIndex = ...,\n raw : bool = ...,\n result_type: None = None,\n args : Any = ...,\n **kwargs : Any\n) -> DataFrame: ...\ndef apply[S2: SeriesDType](\n self : DataFrame,\n f : (...) -> NAType | S2,\n axis : AxisIndex = ...,\n raw : bool = ...,\n result_type: None = None,\n args : Any = ...,\n **kwargs : Any\n) -> Series[S2]: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Mapping[Any, Any],\n axis : AxisIndex = ...,\n raw : bool = ...,\n result_type: None = None,\n args : Any = ...,\n **kwargs : Any\n) -> Series: ...\ndef apply[S2: SeriesDType](\n self : DataFrame,\n f : (...) -> NAType | S2,\n axis : Axis = 0,\n raw : bool = ...,\n args : Any = ...,\n *,\n result_type: Literal['expand', 'reduce'],\n **kwargs : Any\n) -> Series[S2]: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Index | Mapping[Any, Any] | MutableSequence[Any] | Series | ndarray[tuple[int]] | tuple[Any, ...],\n axis : Axis = 0,\n raw : bool = ...,\n args : Any = ...,\n *,\n result_type: Literal['expand'],\n **kwargs : Any\n) -> DataFrame: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Index | Mapping[Any, Any] | MutableSequence[Any] | ndarray[tuple[int]] | tuple[Any, ...],\n axis : Axis = 0,\n raw : bool = ...,\n args : Any = ...,\n *,\n result_type: Literal['reduce'],\n **kwargs : Any\n) -> Series: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Index | Mapping[Any, Any] | MutableSequence[Any] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray[tuple[int]] | str | timedelta | timedelta64 | tuple[Any, ...],\n axis : Axis = 0,\n raw : bool = ...,\n args : Any = ...,\n *,\n result_type: Literal['broadcast'],\n **kwargs : Any\n) -> DataFrame: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Series,\n axis : AxisIndex = 0,\n raw : bool = ...,\n args : Any = ...,\n *,\n result_type: Literal['reduce'],\n **kwargs : Any\n) -> Series: ...\ndef apply[S2: SeriesDType](\n self : DataFrame,\n f : (...) -> NAType | S2,\n raw : bool = ...,\n result_type: None = None,\n args : Any = ...,\n *,\n axis : AxisColumn,\n **kwargs : Any\n) -> Series[S2]: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Index | Mapping[Any, Any] | MutableSequence[Any] | ndarray[tuple[int]] | tuple[Any, ...],\n raw : bool = ...,\n result_type: None = None,\n args : Any = ...,\n *,\n axis : AxisColumn,\n **kwargs : Any\n) -> Series: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Series,\n raw : bool = ...,\n result_type: None = None,\n args : Any = ...,\n *,\n axis : AxisColumn,\n **kwargs : Any\n) -> DataFrame: ...\ndef apply(\n self : DataFrame,\n f : (...) -> Series,\n raw : bool = ...,\n args : Any = ...,\n *,\n axis : AxisColumn,\n result_type: Literal['reduce'],\n **kwargs : Any\n) -> DataFrame: ...", "kind": 2, "label": "apply", "sortText": "0z.9999.apply"}, {"detail": "def asfreq(\n self : DataFrame,\n freq : Frequency,\n method : Literal['backfill', 'bfill', 'ffill', 'pad'] | None = None,\n how : Literal['end', 'start'] | None = ...,\n normalize : bool = False,\n fill_value: Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "asfreq", "sortText": "0z.9999.asfreq"}, {"detail": "def asof(\n self : DataFrame,\n where : ExtensionArray | Index | Sequence[Scalar] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64,\n subset: Hashable | list[Hashable] | None = None\n) -> DataFrame: ...", "kind": 2, "label": "asof", "sortText": "0z.9999.asof"}, {"detail": "def assign(\n self: DataFrame,\n **kwargs: IntoColumn\n) -> DataFrame: ...", "kind": 2, "label": "assign", "sortText": "0z.9999.assign"}, {"detail": "def astype(\n self : DataFrame,\n dtype : Literal[' DataFrame: ...", "kind": 2, "label": "astype", "sortText": "0z.9999.astype"}, {"detail": "_AtIndexerFrame", "kind": 5, "label": "at", "sortText": "0z.9999.at"}, {"detail": "def at_time(\n self: DataFrame,\n time: str | time,\n asof: bool = False,\n axis: Literal['columns', 'index', 0, 1] | None = 0\n) -> DataFrame: ...", "kind": 2, "label": "at_time", "sortText": "0z.9999.at_time"}, {"detail": "dict[Hashable | None, Any]", "kind": 5, "label": "attrs", "sortText": "0z.9999.attrs"}, {"detail": "list[Index]", "kind": 5, "label": "axes", "sortText": "0z.9999.axes"}, {"detail": "def between_time(\n self : DataFrame,\n start_time: str | time,\n end_time : str | time,\n inclusive : IntervalClosedType = 'both',\n axis : Literal['columns', 'index', 0, 1] | None = 0\n) -> DataFrame: ...", "kind": 2, "label": "between_time", "sortText": "0z.9999.between_time"}, {"detail": "\n@overload\ndef bfill(\n self : DataFrame,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n inplace : Literal[True],\n limit : int | None = ...,\n limit_area: Literal['inside', 'outside'] | None = ...\n) -> None: ...\ndef bfill(\n self : DataFrame,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n inplace : Literal[False] = False,\n limit : int | None = ...,\n limit_area: Literal['inside', 'outside'] | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "bfill", "sortText": "0z.9999.bfill"}, {"detail": "def bool(self: DataFrame) -> bool: ...", "kind": 2, "label": "bool", "sortText": "0z.9999.bool"}, {"detail": "\n@overload\ndef boxplot(\n self : DataFrame,\n by : None = None,\n ax : Unknown = None,\n fontsize : float | str | None = None,\n rot : float = 0,\n grid : bool = True,\n figsize : tuple[float, float] | None = None,\n layout : tuple[int, int] | None = None,\n *,\n return_type: Literal['axes'] | None = None,\n backend : str | None = None,\n **kwargs : Any\n) -> Unknown: ...\ndef boxplot(\n self : DataFrame,\n by : None = None,\n ax : Unknown = None,\n fontsize : float | str | None = None,\n rot : float = 0,\n grid : bool = True,\n figsize : tuple[float, float] | None = None,\n layout : tuple[int, int] | None = None,\n *,\n return_type: Literal['dict'],\n backend : str | None = None,\n **kwargs : Any\n) -> dict[str, Unknown]: ...\ndef boxplot(\n self : DataFrame,\n by : None = None,\n ax : Unknown = None,\n fontsize : float | str | None = None,\n rot : float = 0,\n grid : bool = True,\n figsize : tuple[float, float] | None = None,\n layout : tuple[int, int] | None = None,\n *,\n return_type: Literal['both'],\n backend : str | None = None,\n **kwargs : Any\n) -> _BoxPlotT: ...\ndef boxplot(\n self : DataFrame,\n by : Hashable | MutableSequence[Unknown] | ndarray[tuple[int]] | range | tuple[Unknown, ...],\n ax : Unknown = None,\n fontsize : float | str | None = None,\n rot : float = 0,\n grid : bool = True,\n figsize : tuple[float, float] | None = None,\n layout : tuple[int, int] | None = None,\n *,\n return_type: None = None,\n backend : str | None = None,\n **kwargs : Any\n) -> Unknown: ...\ndef boxplot(\n self : DataFrame,\n by : Hashable | MutableSequence[Unknown] | ndarray[tuple[int]] | range | tuple[Unknown, ...],\n ax : Unknown = None,\n fontsize : float | str | None = None,\n rot : float = 0,\n grid : bool = True,\n figsize : tuple[float, float] | None = None,\n layout : tuple[int, int] | None = None,\n *,\n return_type: Literal['axes', 'both', 'dict'],\n backend : str | None = None,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "boxplot", "sortText": "0z.9999.boxplot"}, {"detail": "\n@overload\ndef clip(\n self : DataFrame,\n lower : float | None = ...,\n upper : float | None = ...,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n inplace : Literal[False] = False,\n **kwargs: Any\n) -> DataFrame: ...\ndef clip(\n self : DataFrame,\n lower : AnyArrayLike = ...,\n upper : ExtensionArray | Index | Series | ndarray | None = ...,\n *,\n axis : Axis = ...,\n inplace : Literal[False] = False,\n **kwargs: Any\n) -> DataFrame: ...\ndef clip(\n self : DataFrame,\n lower : ExtensionArray | Index | Series | ndarray | None = ...,\n upper : AnyArrayLike = ...,\n *,\n axis : Axis = ...,\n inplace : Literal[False] = False,\n **kwargs: Any\n) -> DataFrame: ...\ndef clip(\n self : DataFrame,\n lower : None = None,\n upper : None = None,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n inplace : Literal[True],\n **kwargs: Any\n) -> DataFrame: ...\ndef clip(\n self : DataFrame,\n lower : float | None = ...,\n upper : float | None = ...,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n inplace : Literal[True],\n **kwargs: Any\n) -> None: ...\ndef clip(\n self : DataFrame,\n lower : AnyArrayLike = ...,\n upper : ExtensionArray | Index | Series | ndarray | None = ...,\n *,\n axis : Axis = ...,\n inplace : Literal[True],\n **kwargs: Any\n) -> None: ...\ndef clip(\n self : DataFrame,\n lower : ExtensionArray | Index | Series | ndarray | None = ...,\n upper : AnyArrayLike = ...,\n *,\n axis : Axis = ...,\n inplace : Literal[True],\n **kwargs: Any\n) -> None: ...", "kind": 2, "label": "clip", "sortText": "0z.9999.clip"}, {"detail": "Index[str]", "kind": 5, "label": "columns", "sortText": "0z.9999.columns"}, {"detail": "def combine(\n self : DataFrame,\n other : DataFrame,\n func : (...) -> Unknown,\n fill_value: Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = None,\n overwrite : bool = True\n) -> DataFrame: ...", "kind": 2, "label": "combine", "sortText": "0z.9999.combine"}, {"detail": "def combine_first(\n self: DataFrame,\n other: DataFrame\n) -> DataFrame: ...", "kind": 2, "label": "combine_first", "sortText": "0z.9999.combine_first"}, {"detail": "def compare(\n self : DataFrame,\n other : DataFrame,\n align_axis : Axis = 1,\n keep_shape : bool = False,\n keep_equal : bool = False,\n result_names: Suffixes = ...\n) -> DataFrame: ...", "kind": 2, "label": "compare", "sortText": "0z.9999.compare"}, {"detail": "def convert_dtypes(\n self : DataFrame,\n infer_objects : bool = True,\n convert_string : bool = True,\n convert_integer : bool = True,\n convert_boolean : bool = True,\n convert_floating: bool = True,\n dtype_backend : DtypeBackend = 'numpy_nullable'\n) -> DataFrame: ...", "kind": 2, "label": "convert_dtypes", "sortText": "0z.9999.convert_dtypes"}, {"detail": "def copy(\n self: DataFrame,\n deep: bool = True\n) -> DataFrame: ...", "kind": 2, "label": "copy", "sortText": "0z.9999.copy"}, {"detail": "def corr(\n self : DataFrame,\n method : Literal['kendall', 'pearson', 'spearman'] = 'pearson',\n min_periods : int = ...,\n numeric_only: bool = False\n) -> DataFrame: ...", "kind": 2, "label": "corr", "sortText": "0z.9999.corr"}, {"detail": "def corrwith(\n self : DataFrame,\n other : DataFrame | Series,\n axis : Literal['columns', 'index', 0, 1] | None = 0,\n drop : bool = False,\n method : Literal['kendall', 'pearson', 'spearman'] = 'pearson',\n numeric_only: bool = False\n) -> Series: ...", "kind": 2, "label": "corrwith", "sortText": "0z.9999.corrwith"}, {"detail": "def count(\n self : DataFrame,\n axis : Axis = 0,\n numeric_only: bool = False\n) -> Series[int]: ...", "kind": 2, "label": "count", "sortText": "0z.9999.count"}, {"detail": "def cov(\n self : DataFrame,\n min_periods : int | None = None,\n ddof : int = 1,\n numeric_only: bool = False\n) -> DataFrame: ...", "kind": 2, "label": "cov", "sortText": "0z.9999.cov"}, {"detail": "def cummax(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n skipna : bool = True,\n *args : Any,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "cummax", "sortText": "0z.9999.cummax"}, {"detail": "def cummin(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n skipna : bool = True,\n *args : Any,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "cummin", "sortText": "0z.9999.cummin"}, {"detail": "def cumprod(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n skipna : bool = True,\n *args : Any,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "cumprod", "sortText": "0z.9999.cumprod"}, {"detail": "def cumsum(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n skipna : bool = True,\n *args : Any,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "cumsum", "sortText": "0z.9999.cumsum"}, {"detail": "def describe(\n self : DataFrame,\n percentiles: list[float] | None = ...,\n include : Literal['all'] | list[Dtype] | None = ...,\n exclude : list[Dtype] | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "describe", "sortText": "0z.9999.describe"}, {"detail": "def diff(\n self : DataFrame,\n periods: int = 1,\n axis : Axis = 0\n) -> DataFrame: ...", "kind": 2, "label": "diff", "sortText": "0z.9999.diff"}, {"detail": "def truediv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "div", "sortText": "0z.9999.div"}, {"detail": "def truediv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "divide", "sortText": "0z.9999.divide"}, {"detail": "\n@overload\ndef dot(\n self: DataFrame,\n other: DataFrame | ExtensionArray | ndarray\n) -> DataFrame: ...\ndef dot(\n self: DataFrame,\n other: Series\n) -> Series: ...", "kind": 2, "label": "dot", "sortText": "0z.9999.dot"}, {"detail": "\n@overload\ndef drop(\n self : DataFrame,\n labels : Unknown = ...,\n *,\n axis : Unknown = ...,\n index : None,\n columns: Unknown = ...,\n level : Unknown = ...,\n inplace: Unknown = ...,\n errors : Unknown = ...\n) -> Never: ...\ndef drop(\n self : DataFrame,\n labels : Unknown = ...,\n *,\n axis : Unknown = ...,\n index : Unknown = ...,\n columns: None,\n level : Unknown = ...,\n inplace: Unknown = ...,\n errors : Unknown = ...\n) -> Never: ...\ndef drop(\n self : DataFrame,\n labels : None,\n *,\n axis : Unknown = ...,\n index : Unknown = ...,\n columns: Unknown = ...,\n level : Unknown = ...,\n inplace: Unknown = ...,\n errors : Unknown = ...\n) -> Never: ...\ndef drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable] = ...,\n columns: Hashable | Iterable[Hashable],\n level : Hashable | None = ...,\n inplace: Literal[True],\n errors : IgnoreRaise = ...\n) -> None: ...\ndef drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable],\n columns: Hashable | Iterable[Hashable] = ...,\n level : Hashable | None = ...,\n inplace: Literal[True],\n errors : IgnoreRaise = ...\n) -> None: ...\ndef drop(\n self : DataFrame,\n labels : Hashable | Index | Sequence[Hashable],\n *,\n axis : Axis = ...,\n index : None = None,\n columns: None = None,\n level : Hashable | None = ...,\n inplace: Literal[True],\n errors : IgnoreRaise = ...\n) -> None: ...\ndef drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable] = ...,\n columns: Hashable | Iterable[Hashable],\n level : Hashable | None = ...,\n inplace: Literal[False] = False,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...\ndef drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable],\n columns: Hashable | Iterable[Hashable] = ...,\n level : Hashable | None = ...,\n inplace: Literal[False] = False,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...\ndef drop(\n self : DataFrame,\n labels : Hashable | Index | Sequence[Hashable],\n *,\n axis : Axis = ...,\n index : None = None,\n columns: None = None,\n level : Hashable | None = ...,\n inplace: Literal[False] = False,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...", "kind": 2, "label": "drop", "sortText": "0z.9999.drop"}, {"detail": "\n@overload\ndef drop_duplicates(\n self : DataFrame,\n subset : Hashable | Iterable[Hashable] | None = ...,\n *,\n keep : DropKeep = ...,\n inplace : Literal[True],\n ignore_index: bool = ...\n) -> None: ...\ndef drop_duplicates(\n self : DataFrame,\n subset : Hashable | Iterable[Hashable] | None = ...,\n *,\n keep : DropKeep = ...,\n inplace : Literal[False] = False,\n ignore_index: bool = ...\n) -> DataFrame: ...", "kind": 2, "label": "drop_duplicates", "sortText": "0z.9999.drop_duplicates"}, {"detail": "def droplevel(\n self : DataFrame,\n level: Hashable | list[Hashable],\n axis : Axis = 0\n) -> DataFrame: ...", "kind": 2, "label": "droplevel", "sortText": "0z.9999.droplevel"}, {"detail": "\n@overload\ndef dropna(\n self : DataFrame,\n *,\n axis : Axis = 0,\n how : AnyAll = ...,\n thresh : int | None = ...,\n subset : Index | Sequence[Unknown] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray[tuple[int]] | str | timedelta | timedelta64 | None = ...,\n inplace : Literal[True],\n ignore_index: bool = ...\n) -> None: ...\ndef dropna(\n self : DataFrame,\n *,\n axis : Axis = 0,\n how : AnyAll = ...,\n thresh : int | None = ...,\n subset : Index | Sequence[Unknown] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray[tuple[int]] | str | timedelta | timedelta64 | None = ...,\n inplace : Literal[False] = False,\n ignore_index: bool = ...\n) -> DataFrame: ...", "kind": 2, "label": "dropna", "sortText": "0z.9999.dropna"}, {"detail": "Series", "kind": 5, "label": "dtypes", "sortText": "0z.9999.dtypes"}, {"detail": "def duplicated(\n self : DataFrame,\n subset: Hashable | Iterable[Hashable] | None = ...,\n keep : DropKeep = 'first'\n) -> Series: ...", "kind": 2, "label": "duplicated", "sortText": "0z.9999.duplicated"}, {"detail": "bool", "kind": 5, "label": "empty", "sortText": "0z.9999.empty"}, {"detail": "def eq(\n self : DataFrame,\n other: DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level: Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "eq", "sortText": "0z.9999.eq"}, {"detail": "def equals(\n self: DataFrame,\n other: DataFrame | Series\n) -> bool: ...", "kind": 2, "label": "equals", "sortText": "0z.9999.equals"}, {"detail": "\n@overload\ndef eval(\n self: DataFrame,\n expr: str,\n *,\n inplace: Literal[True],\n **kwargs: Any\n) -> None: ...\ndef eval(\n self : DataFrame,\n expr : str,\n *,\n inplace : Literal[False] = False,\n **kwargs: Any\n) -> DataFrame | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64: ...", "kind": 2, "label": "eval", "sortText": "0z.9999.eval"}, {"detail": "def ewm(\n self : DataFrame,\n com : float | None = ...,\n span : float | None = ...,\n halflife : float | None = ...,\n alpha : float | None = ...,\n min_periods: int = 0,\n adjust : bool = True,\n ignore_na : bool = False,\n axis : Axis = 0\n) -> ExponentialMovingWindow[DataFrame]: ...", "kind": 2, "label": "ewm", "sortText": "0z.9999.ewm"}, {"detail": "def expanding(\n self : DataFrame,\n min_periods: int = 1,\n axis : AxisIndex = 0,\n method : CalculationMethod = 'single'\n) -> Expanding[DataFrame]: ...", "kind": 2, "label": "expanding", "sortText": "0z.9999.expanding"}, {"detail": "def explode(\n self : DataFrame,\n column : Sequence[Hashable],\n ignore_index: bool = False\n) -> DataFrame: ...", "kind": 2, "label": "explode", "sortText": "0z.9999.explode"}, {"detail": "\n@overload\ndef ffill(\n self : DataFrame,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n inplace : Literal[True],\n limit : int | None = ...,\n limit_area: Literal['inside', 'outside'] | None = ...\n) -> None: ...\ndef ffill(\n self : DataFrame,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n inplace : Literal[False] = False,\n limit : int | None = ...,\n limit_area: Literal['inside', 'outside'] | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "ffill", "sortText": "0z.9999.ffill"}, {"detail": "\n@overload\ndef fillna(\n self : DataFrame,\n value : DataFrame | NAType | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | dict[Unknown, Unknown] | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n limit : int = ...,\n inplace: Literal[True]\n) -> None: ...\ndef fillna(\n self : DataFrame,\n value : DataFrame | NAType | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | dict[Unknown, Unknown] | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n limit : int = ...,\n inplace: Literal[False] = False\n) -> DataFrame: ...", "kind": 2, "label": "fillna", "sortText": "0z.9999.fillna"}, {"detail": "def filter(\n self : DataFrame,\n items: ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = ...,\n like : str | None = ...,\n regex: str | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = None\n) -> DataFrame: ...", "kind": 2, "label": "filter", "sortText": "0z.9999.filter"}, {"detail": "def first_valid_index(self: DataFrame) -> Scalar: ...", "kind": 2, "label": "first_valid_index", "sortText": "0z.9999.first_valid_index"}, {"detail": "def floordiv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | float | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "floordiv", "sortText": "0z.9999.floordiv"}, {"detail": "\n@overload\ndef from_dict(\n cls : type[DataFrame],\n data : dict[Any, Any],\n orient : Literal['index'],\n dtype : Literal[' DataFrame: ...\ndef from_dict(\n cls : type[DataFrame],\n data : dict[Any, Any],\n orient: Literal['columns', 'tight'] = ...,\n dtype : Literal[' DataFrame: ...", "kind": 2, "label": "from_dict", "sortText": "0z.9999.from_dict"}, {"detail": "def from_records[HashableT: Hashable](\n cls : type[DataFrame],\n data : Iterable[Mapping[HashableT, Any]] | Iterable[SequenceNotStr[Any]] | Mapping[HashableT, SequenceNotStr[Any]] | Mapping[HashableT, Any] | ndarray[tuple[int, int]],\n index : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | str | None = None,\n exclude : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n columns : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n coerce_float: bool = False,\n nrows : int | None = None\n) -> DataFrame: ...", "kind": 2, "label": "from_records", "sortText": "0z.9999.from_records"}, {"detail": "def ge(\n self : DataFrame,\n other: DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level: Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "ge", "sortText": "0z.9999.ge"}, {"detail": "\n@overload\ndef get(\n self : DataFrame,\n key : Hashable,\n default: None = None\n) -> Series | None: ...\ndef get[T](\n self: DataFrame,\n key: Hashable,\n default: T\n) -> Series | T: ...\ndef get(\n self : DataFrame,\n key : list[Hashable],\n default: None = None\n) -> DataFrame | None: ...\ndef get[T](\n self: DataFrame,\n key: list[Hashable],\n default: T\n) -> DataFrame | T: ...", "kind": 2, "label": "get", "sortText": "0z.9999.get"}, {"detail": "\n@overload\ndef groupby(\n self : DataFrame,\n by : Scalar,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Scalar, Literal[True]]: ...\ndef groupby(\n self : DataFrame,\n by : Scalar,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Scalar, Literal[False]]: ...\ndef groupby(\n self : DataFrame,\n by : DatetimeIndex,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Timestamp, Literal[True]]: ...\ndef groupby(\n self : DataFrame,\n by : DatetimeIndex,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Timestamp, Literal[False]]: ...\ndef groupby(\n self : DataFrame,\n by : TimedeltaIndex,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Timedelta, Literal[True]]: ...\ndef groupby(\n self : DataFrame,\n by : TimedeltaIndex,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Timedelta, Literal[False]]: ...\ndef groupby(\n self : DataFrame,\n by : PeriodIndex,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Period, Literal[True]]: ...\ndef groupby(\n self : DataFrame,\n by : PeriodIndex,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Period, Literal[False]]: ...\ndef groupby[IntervalT: Interval[Unknown]](\n self : DataFrame,\n by : IntervalIndex[IntervalT],\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[IntervalT, Literal[True]]: ...\ndef groupby[IntervalT: Interval[Unknown]](\n self : DataFrame,\n by : IntervalIndex[IntervalT],\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[IntervalT, Literal[False]]: ...\ndef groupby(\n self : DataFrame,\n by : ((...) -> Any) | Grouper | Mapping[Hashable | None, Any] | MultiIndex | list[((...) -> Any) | ufunc] | list[Grouper] | list[Index] | list[Mapping[Hashable | None, Any]] | list[Series] | list[ndarray] | list[Unknown] | ndarray | ufunc | tuple[Unknown, ...] | None = ...,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[tuple[Unknown, ...], Literal[True]]: ...\ndef groupby(\n self : DataFrame,\n by : ((...) -> Any) | Grouper | Mapping[Hashable | None, Any] | MultiIndex | list[((...) -> Any) | ufunc] | list[Grouper] | list[Index] | list[Mapping[Hashable | None, Any]] | list[Series] | list[ndarray] | list[Unknown] | ndarray | ufunc | tuple[Unknown, ...] | None = ...,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[tuple[Unknown, ...], Literal[False]]: ...\ndef groupby[SeriesByT: Interval[Timedelta | Timestamp | float | int] | Period | bool | bytes | complex | date | datetime | float | int | str | timedelta](\n self : DataFrame,\n by : Series[SeriesByT],\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[SeriesByT, Literal[True]]: ...\ndef groupby[SeriesByT: Interval[Timedelta | Timestamp | float | int] | Period | bool | bytes | complex | date | datetime | float | int | str | timedelta](\n self : DataFrame,\n by : Series[SeriesByT],\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[SeriesByT, Literal[False]]: ...\ndef groupby(\n self : DataFrame,\n by : CategoricalIndex | Index | Series,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[True] = True,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Any, Literal[True]]: ...\ndef groupby(\n self : DataFrame,\n by : CategoricalIndex | Index | Series,\n level : Hashable | Sequence[Hashable] | None = ...,\n as_index : Literal[False] = False,\n sort : bool = ...,\n group_keys: bool = ...,\n observed : _NoDefault | bool = ...,\n dropna : bool = ...\n) -> DataFrameGroupBy[Any, Literal[False]]: ...", "kind": 2, "label": "groupby", "sortText": "0z.9999.groupby"}, {"detail": "def gt(\n self : DataFrame,\n other: DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level: Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "gt", "sortText": "0z.9999.gt"}, {"detail": "def head(\n self: DataFrame,\n n : int = 5\n) -> DataFrame: ...", "kind": 2, "label": "head", "sortText": "0z.9999.head"}, {"detail": "def hist(\n self : DataFrame,\n by : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | str | None = None,\n bins : int | list[Unknown] = 10,\n *,\n grid : bool = True,\n xlabelsize: float | str | None = None,\n xrot : float | None = None,\n ylabelsize: float | str | None = None,\n yrot : float | None = None,\n ax : Unknown = None,\n sharex : bool = False,\n sharey : bool = False,\n figsize : tuple[float, float] | None = None,\n layout : tuple[int, int] | None = None,\n backend : str | None = None,\n legend : bool = False,\n **kwargs : Any\n) -> ndarray[tuple[Any, ...], dtype[object_]]: ...", "kind": 2, "label": "hist", "sortText": "0z.9999.hist"}, {"detail": "_iAtIndexerFrame", "kind": 5, "label": "iat", "sortText": "0z.9999.iat"}, {"detail": "def idxmax(\n self : DataFrame,\n axis : Axis = 0,\n skipna : bool = True,\n numeric_only: bool = False\n) -> Series[int]: ...", "kind": 2, "label": "idxmax", "sortText": "0z.9999.idxmax"}, {"detail": "def idxmin(\n self : DataFrame,\n axis : Axis = 0,\n skipna : bool = True,\n numeric_only: bool = False\n) -> Series[int]: ...", "kind": 2, "label": "idxmin", "sortText": "0z.9999.idxmin"}, {"detail": "_iLocIndexerFrame[DataFrame]", "kind": 5, "label": "iloc", "sortText": "0z.9999.iloc"}, {"detail": "Index", "kind": 5, "label": "index", "sortText": "0z.9999.index"}, {"detail": "def infer_objects(\n self: DataFrame,\n copy: bool | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "infer_objects", "sortText": "0z.9999.infer_objects"}, {"detail": "def info(\n self : DataFrame,\n verbose : bool | None = ...,\n buf : WriteBuffer[str] | None = ...,\n max_cols : int | None = ...,\n memory_usage: Literal['deep'] | bool | None = ...,\n show_counts : bool | None = ...\n) -> None: ...", "kind": 2, "label": "info", "sortText": "0z.9999.info"}, {"detail": "def insert(\n self : DataFrame,\n loc : int,\n column : Hashable,\n value : Index | Sequence[Unknown] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray[tuple[int]] | str | timedelta | timedelta64 | None,\n allow_duplicates: bool = ...\n) -> None: ...", "kind": 2, "label": "insert", "sortText": "0z.9999.insert"}, {"detail": "\n@overload\ndef interpolate(\n self : DataFrame,\n method : InterpolateOptions = ...,\n *,\n axis : Axis = 0,\n limit : int | None = ...,\n limit_direction: Literal['backward', 'both', 'forward'] = ...,\n limit_area : Literal['inside', 'outside'] | None = ...,\n inplace : Literal[True],\n **kwargs : Any\n) -> None: ...\ndef interpolate(\n self : DataFrame,\n method : InterpolateOptions = ...,\n *,\n axis : Axis = 0,\n limit : int | None = ...,\n limit_direction: Literal['backward', 'both', 'forward'] = ...,\n limit_area : Literal['inside', 'outside'] | None = ...,\n inplace : Literal[False] = False,\n **kwargs : Any\n) -> DataFrame: ...", "kind": 2, "label": "interpolate", "sortText": "0z.9999.interpolate"}, {"detail": "def isetitem(\n self: DataFrame,\n loc: Sequence[int] | int,\n value: ExtensionArray | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | list[Any] | ndarray | str | timedelta | timedelta64\n) -> None: ...", "kind": 2, "label": "isetitem", "sortText": "0z.9999.isetitem"}, {"detail": "def isin(\n self: DataFrame,\n values: DataFrame | Iterable[Any] | Mapping[Hashable, Iterable[Any]]\n) -> DataFrame: ...", "kind": 2, "label": "isin", "sortText": "0z.9999.isin"}, {"detail": "def isna(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "isna", "sortText": "0z.9999.isna"}, {"detail": "def isnull(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "isnull", "sortText": "0z.9999.isnull"}, {"detail": "def items(self: DataFrame) -> Iterator[tuple[Hashable, Series]]: ...", "kind": 2, "label": "items", "sortText": "0z.9999.items"}, {"detail": "def iterrows(self: DataFrame) -> Iterator[tuple[Hashable, Series]]: ...", "kind": 2, "label": "iterrows", "sortText": "0z.9999.iterrows"}, {"detail": "\n@overload\ndef itertuples(\n self : DataFrame,\n index: bool = ...,\n name : str = ...\n) -> Iterator[_PandasNamedTuple]: ...\ndef itertuples(\n self : DataFrame,\n index: bool = ...,\n name : None = None\n) -> Iterator[tuple[Any, ...]]: ...", "kind": 2, "label": "itertuples", "sortText": "0z.9999.itertuples"}, {"detail": "def join(\n self : DataFrame,\n other : DataFrame | Series | list[DataFrame | Series],\n on : list[str] | str | None = ...,\n how : MergeHow = 'left',\n lsuffix : str = '',\n rsuffix : str = '',\n sort : bool = False,\n validate: Literal['1:1', '1:m', 'm:1', 'm:m', 'many_to_many', 'many_to_one', 'one_to_many', 'one_to_one'] | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "join", "sortText": "0z.9999.join"}, {"detail": "def keys(self: DataFrame) -> Index: ...", "kind": 2, "label": "keys", "sortText": "0z.9999.keys"}, {"detail": "def kurt(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "kurt", "sortText": "0z.9999.kurt"}, {"detail": "def kurtosis(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "kurtosis", "sortText": "0z.9999.kurtosis"}, {"detail": "def last_valid_index(self: DataFrame) -> Scalar: ...", "kind": 2, "label": "last_valid_index", "sortText": "0z.9999.last_valid_index"}, {"detail": "def le(\n self : DataFrame,\n other: DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level: Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "le", "sortText": "0z.9999.le"}, {"detail": "_LocIndexerFrame[DataFrame]", "kind": 5, "label": "loc", "sortText": "0z.9999.loc"}, {"detail": "def lt(\n self : DataFrame,\n other: DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level: Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "lt", "sortText": "0z.9999.lt"}, {"detail": "def map(\n self : DataFrame,\n func : (...) -> Unknown,\n na_action: Literal['ignore'] | None = None,\n **kwargs : Any\n) -> DataFrame: ...", "kind": 2, "label": "map", "sortText": "0z.9999.map"}, {"detail": "\n@overload\ndef mask(\n self : DataFrame,\n cond : ((DataFrame) -> DataFrame) | ((Any) -> builtins.bool) | DataFrame | Series | ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other : ((...) -> Unknown) | DataFrame | NAType | Series | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n *,\n inplace: Literal[True],\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n level : Hashable | None = None\n) -> None: ...\ndef mask(\n self : DataFrame,\n cond : ((DataFrame) -> DataFrame) | ((Any) -> builtins.bool) | DataFrame | Series | ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other : ((...) -> Unknown) | DataFrame | NAType | Series | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n *,\n inplace: Literal[False] = False,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n level : Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "mask", "sortText": "0z.9999.mask"}, {"detail": "\n@overload\ndef max(\n self : DataFrame,\n axis : None,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Scalar: ...\ndef max(\n self : DataFrame,\n axis : Axis = 0,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "max", "sortText": "0z.9999.max"}, {"detail": "\n@overload\ndef mean(\n self : DataFrame,\n axis : None,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Scalar: ...\ndef mean(\n self : DataFrame,\n axis : Axis = 0,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "mean", "sortText": "0z.9999.mean"}, {"detail": "\n@overload\ndef median(\n self : DataFrame,\n axis : None,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Scalar: ...\ndef median(\n self : DataFrame,\n axis : Axis = 0,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "median", "sortText": "0z.9999.median"}, {"detail": "def melt(\n self : DataFrame,\n id_vars : Sequence[Unknown] | ndarray | tuple[Unknown, ...] | None = ...,\n value_vars : Sequence[Unknown] | ndarray | tuple[Unknown, ...] | None = ...,\n var_name : Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = None,\n value_name : Scalar = 'value',\n col_level : int | str | None = ...,\n ignore_index: bool = True\n) -> DataFrame: ...", "kind": 2, "label": "melt", "sortText": "0z.9999.melt"}, {"detail": "def memory_usage(\n self : DataFrame,\n index: bool = True,\n deep : bool = False\n) -> Series: ...", "kind": 2, "label": "memory_usage", "sortText": "0z.9999.memory_usage"}, {"detail": "def merge(\n self : DataFrame,\n right : DataFrame | Series,\n how : MergeHow = 'inner',\n on : ExtensionArray | Hashable | Index | Sequence[Hashable] | Series | ndarray | None = ...,\n left_on : ExtensionArray | Hashable | Index | Sequence[Hashable] | Series | ndarray | None = ...,\n right_on : ExtensionArray | Hashable | Index | Sequence[Hashable] | Series | ndarray | None = ...,\n left_index : bool = False,\n right_index: bool = False,\n sort : bool = False,\n suffixes : Suffixes = ...,\n copy : bool = True,\n indicator : bool | str = False,\n validate : Literal['1:1', '1:m', 'm:1', 'm:m', 'many_to_many', 'many_to_one', 'one_to_many', 'one_to_one'] | None = None\n) -> DataFrame: ...", "kind": 2, "label": "merge", "sortText": "0z.9999.merge"}, {"detail": "\n@overload\ndef min(\n self : DataFrame,\n axis : None,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Scalar: ...\ndef min(\n self : DataFrame,\n axis : Axis = 0,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "min", "sortText": "0z.9999.min"}, {"detail": "def mod(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | float | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "mod", "sortText": "0z.9999.mod"}, {"detail": "def mode(\n self : DataFrame,\n axis : Axis = 0,\n numeric_only: bool = False,\n dropna : bool = True\n) -> Series: ...", "kind": 2, "label": "mode", "sortText": "0z.9999.mode"}, {"detail": "def mul(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "mul", "sortText": "0z.9999.mul"}, {"detail": "def mul(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "multiply", "sortText": "0z.9999.multiply"}, {"detail": "int", "kind": 5, "label": "ndim", "sortText": "0z.9999.ndim"}, {"detail": "def ne(\n self : DataFrame,\n other: DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level: Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "ne", "sortText": "0z.9999.ne"}, {"detail": "def nlargest(\n self : DataFrame,\n n : int,\n columns: list[str] | str,\n keep : NsmallestNlargestKeep = 'first'\n) -> DataFrame: ...", "kind": 2, "label": "nlargest", "sortText": "0z.9999.nlargest"}, {"detail": "def notna(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "notna", "sortText": "0z.9999.notna"}, {"detail": "def notnull(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "notnull", "sortText": "0z.9999.notnull"}, {"detail": "def nsmallest(\n self : DataFrame,\n n : int,\n columns: list[str] | str,\n keep : NsmallestNlargestKeep = 'first'\n) -> DataFrame: ...", "kind": 2, "label": "nsmallest", "sortText": "0z.9999.nsmallest"}, {"detail": "def nunique(\n self : DataFrame,\n axis : Axis = 0,\n dropna: bool = True\n) -> Series[int]: ...", "kind": 2, "label": "nunique", "sortText": "0z.9999.nunique"}, {"detail": "def pct_change(\n self : DataFrame,\n periods : int = 1,\n fill_method: None = None,\n freq : BaseOffset | str | timedelta | None = ...,\n fill_value : NAType | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "pct_change", "sortText": "0z.9999.pct_change"}, {"detail": "\n@overload\ndef pipe[**P, T](\n DataFrame,\n (\n DataFrame,\n ParamSpec(P)\n ) -> T,\n ParamSpec(P)\n) -> T: ...\ndef pipe[T](\n self: DataFrame,\n func: tuple[(...) -> T, str],\n *args: Any,\n **kwargs: Any\n) -> T: ...", "kind": 2, "label": "pipe", "sortText": "0z.9999.pipe"}, {"detail": "def pivot(\n self : DataFrame,\n *,\n columns: IndexLabel,\n index : IndexLabel = ...,\n values : IndexLabel = ...\n) -> DataFrame: ...", "kind": 2, "label": "pivot", "sortText": "0z.9999.pivot"}, {"detail": "def pivot_table(\n self : DataFrame,\n values : _PivotTableValuesTypes = None,\n index : _PivotTableIndexTypes = None,\n columns : _PivotTableColumnsTypes = None,\n aggfunc : Literal['count', 'max', 'mean', 'median', 'min', 'std', 'sum', 'var'] | ((Series) -> Unknown) | Mapping[Hashable, _PivotAggFunc] | Sequence[_PivotAggFunc] | ufunc = 'mean',\n fill_value : Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = None,\n margins : bool = False,\n dropna : bool = True,\n margins_name: str = 'All',\n observed : bool = True,\n sort : bool = True\n) -> DataFrame: ...", "kind": 2, "label": "pivot_table", "sortText": "0z.9999.pivot_table"}, {"detail": "PlotAccessor", "kind": 5, "label": "plot", "sortText": "0z.9999.plot"}, {"detail": "def pop(\n self: DataFrame,\n item: str\n) -> Series: ...", "kind": 2, "label": "pop", "sortText": "0z.9999.pop"}, {"detail": "def pow(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "pow", "sortText": "0z.9999.pow"}, {"detail": "def prod(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n skipna : bool | None = True,\n numeric_only: bool = False,\n min_count : int = 0,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "prod", "sortText": "0z.9999.prod"}, {"detail": "def prod(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n skipna : bool | None = True,\n numeric_only: bool = False,\n min_count : int = 0,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "product", "sortText": "0z.9999.product"}, {"detail": "\n@overload\ndef quantile(\n self : DataFrame,\n q : float = ...,\n axis : Axis = 0,\n numeric_only : bool = ...,\n interpolation: QuantileInterpolation = ...,\n method : CalculationMethod = ...\n) -> Series: ...\ndef quantile(\n self : DataFrame,\n q : Sequence[float] | ndarray[tuple[Any, ...], dtype[floating]],\n axis : Axis = 0,\n numeric_only : bool = ...,\n interpolation: QuantileInterpolation = ...,\n method : CalculationMethod = ...\n) -> DataFrame: ...", "kind": 2, "label": "quantile", "sortText": "0z.9999.quantile"}, {"detail": "\n@overload\ndef query(\n self : DataFrame,\n expr : str,\n *,\n parser : Literal['pandas', 'python'] = ...,\n engine : Literal['numexpr', 'python'] | None = ...,\n local_dict : dict[str, Any] | None = ...,\n global_dict: dict[str, Any] | None = ...,\n resolvers : list[Mapping[Unknown, Unknown]] | None = ...,\n level : int = ...,\n target : object | None = ...,\n inplace : Literal[True]\n) -> None: ...\ndef query(\n self : DataFrame,\n expr : str,\n *,\n inplace : Literal[False] = False,\n parser : Literal['pandas', 'python'] = ...,\n engine : Literal['numexpr', 'python'] | None = ...,\n local_dict : dict[str, Any] | None = ...,\n global_dict: dict[str, Any] | None = ...,\n resolvers : list[Mapping[Unknown, Unknown]] | None = ...,\n level : int = ...,\n target : object | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "query", "sortText": "0z.9999.query"}, {"detail": "def radd(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "radd", "sortText": "0z.9999.radd"}, {"detail": "def rank(\n self : DataFrame,\n axis : Axis = 0,\n method : Literal['average', 'dense', 'first', 'max', 'min'] = 'average',\n numeric_only: bool = False,\n na_option : Literal['bottom', 'keep', 'top'] = 'keep',\n ascending : bool = True,\n pct : bool = False\n) -> DataFrame: ...", "kind": 2, "label": "rank", "sortText": "0z.9999.rank"}, {"detail": "def rtruediv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rdiv", "sortText": "0z.9999.rdiv"}, {"detail": "def reindex(\n self : DataFrame,\n labels : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = ...,\n *,\n index : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = ...,\n columns : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n method : Literal['backfill', 'bfill', 'ffill', 'nearest', 'pad'] | None = ...,\n copy : bool = True,\n level : int | str = ...,\n fill_value: Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n limit : int | None = None,\n tolerance : Timedelta | float | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "reindex", "sortText": "0z.9999.reindex"}, {"detail": "def reindex_like(\n self : DataFrame,\n other : DataFrame,\n method : Literal['backfill', 'bfill', 'ffill', 'nearest', 'pad'] | None = ...,\n copy : bool = True,\n limit : int | None = None,\n tolerance: ExtensionArray | Index | Sequence[Scalar] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64 = ...\n) -> DataFrame: ...", "kind": 2, "label": "reindex_like", "sortText": "0z.9999.reindex_like"}, {"detail": "\n@overload\ndef rename(\n self : DataFrame,\n mapper : ((Any) -> Hashable | None) | Mapping[Any, Hashable | None] | None = ...,\n *,\n index : ((Any) -> Hashable | None) | Mapping[Any, Hashable | None] | None = ...,\n columns: ((Any) -> Hashable | None) | Mapping[Any, Hashable | None] | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n copy : bool = ...,\n inplace: Literal[True],\n level : Hashable | None = None,\n errors : IgnoreRaise = ...\n) -> None: ...\ndef rename(\n self : DataFrame,\n mapper : ((Any) -> Hashable | None) | Mapping[Any, Hashable | None] | None = ...,\n *,\n index : ((Any) -> Hashable | None) | Mapping[Any, Hashable | None] | None = ...,\n columns: ((Any) -> Hashable | None) | Mapping[Any, Hashable | None] | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n copy : bool = ...,\n inplace: Literal[False] = False,\n level : Hashable | None = None,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...", "kind": 2, "label": "rename", "sortText": "0z.9999.rename"}, {"detail": "\n@overload\ndef rename_axis(\n self : DataFrame,\n mapper : ExtensionArray | Index | SequenceNotStr[Any] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | range | str | timedelta | timedelta64 | None = ...,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n copy : bool = ...,\n inplace: Literal[True]\n) -> None: ...\ndef rename_axis(\n self : DataFrame,\n mapper : ExtensionArray | Index | SequenceNotStr[Any] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | range | str | timedelta | timedelta64 | None = ...,\n *,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n copy : bool = ...,\n inplace: Literal[False] = False\n) -> DataFrame: ...\ndef rename_axis(\n self : DataFrame,\n *,\n index : ((...) -> Unknown) | Sequence[str] | dict[int | str, str] | str | None = ...,\n columns: ((...) -> Unknown) | Sequence[str] | dict[int | str, str] | str | None = ...,\n copy : bool = ...,\n inplace: Literal[True]\n) -> None: ...\ndef rename_axis(\n self : DataFrame,\n *,\n index : ((...) -> Unknown) | Sequence[str] | dict[int | str, str] | str | None = ...,\n columns: ((...) -> Unknown) | Sequence[str] | dict[int | str, str] | str | None = ...,\n copy : bool = ...,\n inplace: Literal[False] = False\n) -> DataFrame: ...", "kind": 2, "label": "rename_axis", "sortText": "0z.9999.rename_axis"}, {"detail": "def reorder_levels(\n self : DataFrame,\n order: list[Unknown],\n axis : Axis = 0\n) -> DataFrame: ...", "kind": 2, "label": "reorder_levels", "sortText": "0z.9999.reorder_levels"}, {"detail": "\n@overload\ndef replace[HashableT2: Hashable, HashableT3: Hashable](\n self : DataFrame,\n to_replace: Mapping[HashableT2, ReplaceValue] | Mapping[Unknown, Unknown] | NAType | Pattern[Unknown] | Sequence[Pattern[Unknown] | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n value : Mapping[HashableT3, ReplaceValue] | Mapping[Unknown, Unknown] | NAType | Pattern[Unknown] | Sequence[Pattern[Unknown] | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n *,\n inplace : Literal[True],\n regex : Mapping[HashableT3, ReplaceValue] | Mapping[Unknown, Unknown] | NAType | Pattern[Unknown] | Sequence[Pattern[Unknown] | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...\n) -> None: ...\ndef replace[HashableT2: Hashable, HashableT3: Hashable](\n self : DataFrame,\n to_replace: Mapping[HashableT2, ReplaceValue] | Mapping[Unknown, Unknown] | NAType | Pattern[Unknown] | Sequence[Pattern[Unknown] | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n value : Mapping[HashableT3, ReplaceValue] | Mapping[Unknown, Unknown] | NAType | Pattern[Unknown] | Sequence[Pattern[Unknown] | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...,\n *,\n inplace : Literal[False] = False,\n regex : Mapping[HashableT3, ReplaceValue] | Mapping[Unknown, Unknown] | NAType | Pattern[Unknown] | Sequence[Pattern[Unknown] | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "replace", "sortText": "0z.9999.replace"}, {"detail": "def resample(\n self : DataFrame,\n rule : BaseOffset | str | timedelta,\n axis : Literal['columns', 'index', 0, 1] | _NoDefault = 0,\n closed : Literal['left', 'right'] | None = None,\n label : Literal['left', 'right'] | None = None,\n on : Hashable | None = None,\n level : Hashable | None = None,\n origin : Timestamp | date | datetime | datetime64 | float | integer | str = 'start_day',\n offset : Timedelta | float | integer | str | timedelta | timedelta64 | None = None,\n group_keys: bool = False\n) -> DatetimeIndexResampler[DataFrame]: ...", "kind": 2, "label": "resample", "sortText": "0z.9999.resample"}, {"detail": "\n@overload\ndef reset_index(\n self : DataFrame,\n level : Hashable | Sequence[Hashable] = ...,\n *,\n drop : bool = ...,\n col_level : int | str = ...,\n col_fill : Hashable = ...,\n inplace : Literal[True],\n allow_duplicates: bool = ...,\n names : Hashable | Sequence[Hashable] = ...\n) -> None: ...\ndef reset_index(\n self : DataFrame,\n level : Hashable | Sequence[Hashable] = ...,\n *,\n col_level : int | str = ...,\n col_fill : Hashable = ...,\n drop : bool = ...,\n inplace : Literal[False] = False,\n allow_duplicates: bool = ...,\n names : Hashable | Sequence[Hashable] = ...\n) -> DataFrame: ...", "kind": 2, "label": "reset_index", "sortText": "0z.9999.reset_index"}, {"detail": "def rfloordiv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rfloordiv", "sortText": "0z.9999.rfloordiv"}, {"detail": "def rmod(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rmod", "sortText": "0z.9999.rmod"}, {"detail": "def rmul(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rmul", "sortText": "0z.9999.rmul"}, {"detail": "\n@overload\ndef rolling(\n self : DataFrame,\n window : BaseIndexer | BaseOffset | int | str | timedelta,\n min_periods: int | None = ...,\n center : bool = ...,\n on : Hashable | None = ...,\n axis : AxisIndex = ...,\n closed : Literal['both', 'left', 'neither', 'right'] | None = ...,\n step : int | None = ...,\n method : CalculationMethod = ...,\n *,\n win_type : str\n) -> Window[DataFrame]: ...\ndef rolling(\n self : DataFrame,\n window : BaseIndexer | BaseOffset | int | str | timedelta,\n min_periods: int | None = ...,\n center : bool = ...,\n on : Hashable | None = ...,\n axis : AxisIndex = ...,\n closed : Literal['both', 'left', 'neither', 'right'] | None = ...,\n step : int | None = ...,\n method : CalculationMethod = ...,\n *,\n win_type : None = None\n) -> Rolling[DataFrame]: ...", "kind": 2, "label": "rolling", "sortText": "0z.9999.rolling"}, {"detail": "def round(\n self : DataFrame,\n decimals: Series | dict[Unknown, Unknown] | int = ...,\n *args : Any,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "round", "sortText": "0z.9999.round"}, {"detail": "def rpow(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rpow", "sortText": "0z.9999.rpow"}, {"detail": "def rsub(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rsub", "sortText": "0z.9999.rsub"}, {"detail": "def rtruediv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "rtruediv", "sortText": "0z.9999.rtruediv"}, {"detail": "def sample(\n self : DataFrame,\n n : int | None = ...,\n frac : float | None = ...,\n replace : bool = False,\n weights : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | str | None = ...,\n random_state: BitGenerator | ExtensionArray | Generator | RandomState | int | ndarray | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n ignore_index: bool = False\n) -> DataFrame: ...", "kind": 2, "label": "sample", "sortText": "0z.9999.sample"}, {"detail": "\n@overload\ndef select_dtypes(\n self : DataFrame,\n include: StrDtypeArg,\n exclude: Literal[' Never: ...\ndef select_dtypes(\n self: DataFrame,\n include: Literal[' Never: ...\ndef select_dtypes(\n self: DataFrame,\n exclude: StrDtypeArg\n) -> Never: ...\ndef select_dtypes(\n self: DataFrame,\n include: list[Never],\n exclude: list[Never]\n) -> Never: ...\ndef select_dtypes(\n self : DataFrame,\n include: _AstypeArgExtList,\n exclude: Literal[' DataFrame: ...\ndef select_dtypes(\n self: DataFrame,\n include: Literal[' DataFrame: ...\ndef select_dtypes(\n self: DataFrame,\n exclude: _AstypeArgExtList\n) -> DataFrame: ...", "kind": 2, "label": "select_dtypes", "sortText": "0z.9999.select_dtypes"}, {"detail": "def sem(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n skipna : bool | None = True,\n ddof : int = 1,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "sem", "sortText": "0z.9999.sem"}, {"detail": "def set_axis(\n self : DataFrame,\n labels: AxesData,\n *,\n axis : Axis = 0\n) -> DataFrame: ...", "kind": 2, "label": "set_axis", "sortText": "0z.9999.set_axis"}, {"detail": "def set_flags(\n self : DataFrame,\n *,\n copy : bool = ...,\n allows_duplicate_labels: bool | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "set_flags", "sortText": "0z.9999.set_flags"}, {"detail": "\n@overload\ndef set_index(\n self : DataFrame,\n keys : Hashable | Index | Iterator[Hashable] | Sequence[Hashable] | Series | ndarray | None,\n *,\n drop : bool = ...,\n append : bool = ...,\n verify_integrity: bool = ...,\n inplace : Literal[True]\n) -> None: ...\ndef set_index(\n self : DataFrame,\n keys : Hashable | Index | Iterator[Hashable] | Sequence[Hashable] | Series | ndarray | None,\n *,\n drop : bool = ...,\n append : bool = ...,\n verify_integrity: bool = ...,\n inplace : Literal[False] = False\n) -> DataFrame: ...", "kind": 2, "label": "set_index", "sortText": "0z.9999.set_index"}, {"detail": "tuple[int, int]", "kind": 5, "label": "shape", "sortText": "0z.9999.shape"}, {"detail": "def shift(\n self : DataFrame,\n periods : Sequence[int] | int = ...,\n freq : BaseOffset | str | timedelta | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = None,\n fill_value: NAType | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = ...\n) -> DataFrame: ...", "kind": 2, "label": "shift", "sortText": "0z.9999.shift"}, {"detail": "int", "kind": 5, "label": "size", "sortText": "0z.9999.size"}, {"detail": "def skew(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n skipna : bool | None = True,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "skew", "sortText": "0z.9999.skew"}, {"detail": "\n@overload\ndef sort_index(\n self : DataFrame,\n *,\n axis : Axis = 0,\n level : Hashable | None = None,\n ascending : Sequence[bool] | bool = ...,\n kind : SortKind = ...,\n na_position : NaPosition = ...,\n sort_remaining: bool = ...,\n ignore_index : bool = ...,\n inplace : Literal[True],\n key : IndexKeyFunc = ...\n) -> None: ...\ndef sort_index(\n self : DataFrame,\n *,\n axis : Axis = 0,\n level : Hashable | list[int] | list[str] | None = ...,\n ascending : Sequence[bool] | bool = ...,\n kind : SortKind = ...,\n na_position : NaPosition = ...,\n sort_remaining: bool = ...,\n ignore_index : bool = ...,\n inplace : Literal[False] = False,\n key : IndexKeyFunc = ...\n) -> DataFrame: ...", "kind": 2, "label": "sort_index", "sortText": "0z.9999.sort_index"}, {"detail": "\n@overload\ndef sort_values(\n self : DataFrame,\n by : Sequence[str] | str,\n *,\n axis : Axis = 0,\n ascending : Sequence[bool] | bool = ...,\n kind : SortKind = ...,\n na_position : NaPosition = ...,\n ignore_index: bool = ...,\n inplace : Literal[True],\n key : ValueKeyFunc = ...\n) -> None: ...\ndef sort_values(\n self : DataFrame,\n by : Sequence[str] | str,\n *,\n axis : Axis = 0,\n ascending : Sequence[bool] | bool = ...,\n kind : SortKind = ...,\n na_position : NaPosition = ...,\n ignore_index: bool = ...,\n inplace : Literal[False] = False,\n key : ValueKeyFunc = ...\n) -> DataFrame: ...", "kind": 2, "label": "sort_values", "sortText": "0z.9999.sort_values"}, {"detail": "EllipsisType", "kind": 5, "label": "sparse", "sortText": "0z.9999.sparse"}, {"detail": "def squeeze(\n self: DataFrame,\n axis: Literal['columns', 'index', 0, 1] | None = None\n) -> DataFrame | Series | Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64: ...", "kind": 2, "label": "squeeze", "sortText": "0z.9999.squeeze"}, {"detail": "\n@overload\ndef stack(\n self : DataFrame,\n level : IndexLabel = ...,\n *,\n future_stack: Literal[True]\n) -> DataFrame | Series: ...\ndef stack(\n self : DataFrame,\n level : IndexLabel = ...,\n dropna : bool = ...,\n sort : bool = ...,\n future_stack: Literal[False] = False\n) -> DataFrame | Series: ...", "kind": 2, "label": "stack", "sortText": "0z.9999.stack"}, {"detail": "def std(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = 0,\n skipna : bool = True,\n ddof : int = 1,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "std", "sortText": "0z.9999.std"}, {"detail": "Styler", "kind": 5, "label": "style", "sortText": "0z.9999.style"}, {"detail": "def sub(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "sub", "sortText": "0z.9999.sub"}, {"detail": "def sub(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Axis = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "subtract", "sortText": "0z.9999.subtract"}, {"detail": "def sum(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = 0,\n skipna : bool | None = True,\n numeric_only: bool = False,\n min_count : int = 0,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "sum", "sortText": "0z.9999.sum"}, {"detail": "def swapaxes(\n self : DataFrame,\n axis1: Axis,\n axis2: Axis,\n copy : bool = ...\n) -> DataFrame: ...", "kind": 2, "label": "swapaxes", "sortText": "0z.9999.swapaxes"}, {"detail": "def swaplevel(\n self: DataFrame,\n i : Hashable = ...,\n j : Hashable = ...,\n axis: Axis = 0\n) -> DataFrame: ...", "kind": 2, "label": "swaplevel", "sortText": "0z.9999.swaplevel"}, {"detail": "def tail(\n self: DataFrame,\n n : int = 5\n) -> DataFrame: ...", "kind": 2, "label": "tail", "sortText": "0z.9999.tail"}, {"detail": "def take(\n self : DataFrame,\n indices : TakeIndexer,\n axis : Axis = 0,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "take", "sortText": "0z.9999.take"}, {"detail": "def to_clipboard[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n excel : bool = True,\n sep : str | None = None,\n *,\n na_rep : str = ...,\n float_format : ((object) -> str) | str | None = ...,\n columns : list[HashableT1] | None = ...,\n header : bool | list[str] = ...,\n index : bool = ...,\n index_label : Literal[False] | list[HashableT2] | str | None = ...,\n mode : FileWriteMode = ...,\n encoding : str | None = ...,\n compression : CompressionOptions = ...,\n quoting : CSVQuoting = ...,\n quotechar : str = ...,\n lineterminator : str | None = ...,\n chunksize : int | None = ...,\n date_format : str | None = ...,\n doublequote : bool = ...,\n escapechar : str | None = ...,\n decimal : str = ...,\n errors : str = ...,\n storage_options: StorageOptions = ...\n) -> None: ...", "kind": 2, "label": "to_clipboard", "sortText": "0z.9999.to_clipboard"}, {"detail": "\n@overload\ndef to_csv[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n path_or_buf : PathLike[str] | WriteBuffer[bytes] | WriteBuffer[str] | str,\n sep : str = ...,\n na_rep : str = ...,\n float_format : ((object) -> str) | str | None = ...,\n columns : list[HashableT1] | None = ...,\n header : bool | list[str] = ...,\n index : bool = ...,\n index_label : Literal[False] | list[HashableT2] | str | None = ...,\n mode : FileWriteMode = ...,\n encoding : str | None = ...,\n compression : CompressionOptions = ...,\n quoting : CSVQuoting = ...,\n quotechar : str = ...,\n lineterminator : str | None = ...,\n chunksize : int | None = ...,\n date_format : str | None = ...,\n doublequote : bool = ...,\n escapechar : str | None = ...,\n decimal : str = ...,\n errors : OpenFileErrors = ...,\n storage_options: StorageOptions = ...\n) -> None: ...\ndef to_csv[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n path_or_buf : None = None,\n sep : str = ...,\n na_rep : str = ...,\n float_format : ((object) -> str) | str | None = ...,\n columns : list[HashableT1] | None = ...,\n header : bool | list[str] = ...,\n index : bool = ...,\n index_label : Literal[False] | list[HashableT2] | str | None = ...,\n mode : FileWriteMode = ...,\n encoding : str | None = ...,\n compression : CompressionOptions = ...,\n quoting : CSVQuoting = ...,\n quotechar : str = ...,\n lineterminator : str | None = ...,\n chunksize : int | None = ...,\n date_format : str | None = ...,\n doublequote : bool = ...,\n escapechar : str | None = ...,\n decimal : str = ...,\n errors : OpenFileErrors = ...,\n storage_options: StorageOptions = ...\n) -> str: ...", "kind": 2, "label": "to_csv", "sortText": "0z.9999.to_csv"}, {"detail": "\n@overload\ndef to_dict(\n self : DataFrame,\n orient: str = ...,\n *,\n into : type[defaultdict[Unknown, Unknown]],\n index : Literal[True] = True\n) -> Never: ...\ndef to_dict[_T_MUTABLE_MAPPING_co: MutableMapping[Unknown, Unknown]](\n self : DataFrame,\n orient: Literal['records'],\n *,\n into : _T_MUTABLE_MAPPING_co | type[_T_MUTABLE_MAPPING_co],\n index : Literal[True] = True\n) -> list[_T_MUTABLE_MAPPING_co]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['records'],\n *,\n into : type[dict[Unknown, Unknown]] = ...,\n index : Literal[True] = True\n) -> list[dict[Hashable, Any]]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['index'],\n *,\n into : defaultdict[Unknown, Unknown],\n index : Literal[True] = True\n) -> defaultdict[Hashable, dict[Hashable, Any]]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['index'],\n *,\n into : OrderedDict[Unknown, Unknown] | type[OrderedDict[Unknown, Unknown]],\n index : Literal[True] = True\n) -> OrderedDict[Hashable, dict[Hashable, Any]]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['index'],\n *,\n into : type[MutableMapping[Unknown, Unknown]],\n index : Literal[True] = True\n) -> MutableMapping[Hashable, dict[Hashable, Any]]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['index'],\n *,\n into : type[dict[Unknown, Unknown]] = ...,\n index : Literal[True] = True\n) -> dict[Hashable, dict[Hashable, Any]]: ...\ndef to_dict[_T_MUTABLE_MAPPING_co: MutableMapping[Unknown, Unknown]](\n self : DataFrame,\n orient: Literal['dict', 'list', 'series'] = ...,\n *,\n into : _T_MUTABLE_MAPPING_co | type[_T_MUTABLE_MAPPING_co],\n index : Literal[True] = True\n) -> _T_MUTABLE_MAPPING_co: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['dict', 'list', 'series'] = ...,\n *,\n into : type[dict[Unknown, Unknown]] = ...,\n index : Literal[True] = True\n) -> dict[Hashable, Any]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['split', 'tight'],\n *,\n into : MutableMapping[Unknown, Unknown] | type[MutableMapping[Unknown, Unknown]],\n index : bool = ...\n) -> MutableMapping[str, list[Unknown]]: ...\ndef to_dict(\n self : DataFrame,\n orient: Literal['split', 'tight'],\n *,\n into : type[dict[Unknown, Unknown]] = ...,\n index : bool = ...\n) -> dict[str, list[Unknown]]: ...", "kind": 2, "label": "to_dict", "sortText": "0z.9999.to_dict"}, {"detail": "def to_excel(\n self : DataFrame,\n excel_writer: Unknown,\n sheet_name : str = 'Sheet1',\n na_rep : str = '',\n float_format: str | None = ...,\n columns : Sequence[str] | str | None = ...,\n header : bool | list[str] = True,\n index : bool = True,\n index_label : Sequence[str] | str | None = ...,\n startrow : int = 0,\n startcol : int = 0,\n engine : str | None = ...,\n merge_cells : ExcelWriterMergeCells = True,\n inf_rep : str = 'inf',\n freeze_panes: tuple[int, int] | None = ...\n) -> None: ...", "kind": 2, "label": "to_excel", "sortText": "0z.9999.to_excel"}, {"detail": "def to_feather(\n self: DataFrame,\n path: PathLike[str] | WriteBuffer[bytes] | str,\n **kwargs: Any\n) -> None: ...", "kind": 2, "label": "to_feather", "sortText": "0z.9999.to_feather"}, {"detail": "def to_hdf[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n path_or_buf : HDFStore | PathLike[str] | str,\n *,\n key : str,\n mode : Literal['a', 'r+', 'w'] = ...,\n complevel : int | None = ...,\n complib : Literal['blosc', 'bzip2', 'lzo', 'zlib'] | None = ...,\n append : bool = ...,\n format : Literal['f', 'fixed', 't', 'table'] | None = ...,\n index : bool = ...,\n min_itemsize: dict[HashableT1, int] | int | None = ...,\n nan_rep : str | None = ...,\n dropna : bool | None = ...,\n data_columns: Literal[True] | list[HashableT2] | None = ...,\n errors : OpenFileErrors = ...,\n encoding : str = ...\n) -> None: ...", "kind": 2, "label": "to_hdf", "sortText": "0z.9999.to_hdf"}, {"detail": "\n@overload\ndef to_html(\n self : DataFrame,\n buf : PathLike[str] | WriteBuffer[str] | str,\n *,\n columns : Index | SequenceNotStr[Hashable] | Series | None = ...,\n col_space : Mapping[Hashable, int | str] | Sequence[int | str] | int | str | None = ...,\n header : bool = ...,\n index : bool = ...,\n na_rep : str = ...,\n formatters : Mapping[Hashable, (object) -> str] | list[(object) -> str] | tuple[(object) -> str, ...] | None = ...,\n float_format : ((float) -> str) | None = ...,\n sparsify : bool | None = ...,\n index_names : bool = ...,\n justify : Literal['center', 'end', 'inherit', 'initial', 'justify', 'justify-all', 'left', 'match-parent', 'right', 'start', 'unset'] | None = ...,\n max_rows : int | None = ...,\n max_cols : int | None = ...,\n show_dimensions: bool = ...,\n decimal : str = ...,\n bold_rows : bool = ...,\n classes : Sequence[str] | None = ...,\n escape : bool = ...,\n notebook : bool = ...,\n border : int | None = ...,\n table_id : str | None = ...,\n render_links : bool = ...,\n encoding : str | None = ...\n) -> None: ...\ndef to_html(\n self : DataFrame,\n buf : None = None,\n *,\n columns : Sequence[Hashable] | None = ...,\n col_space : Mapping[Hashable, int | str] | Sequence[int | str] | int | str | None = ...,\n header : bool = ...,\n index : bool = ...,\n na_rep : str = ...,\n formatters : Mapping[Hashable, (object) -> str] | list[(object) -> str] | tuple[(object) -> str, ...] | None = ...,\n float_format : ((float) -> str) | None = ...,\n sparsify : bool | None = ...,\n index_names : bool = ...,\n justify : Literal['center', 'end', 'inherit', 'initial', 'justify', 'justify-all', 'left', 'match-parent', 'right', 'start', 'unset'] | None = ...,\n max_rows : int | None = ...,\n max_cols : int | None = ...,\n show_dimensions: bool = ...,\n decimal : str = ...,\n bold_rows : bool = ...,\n classes : Sequence[str] | None = ...,\n escape : bool = ...,\n notebook : bool = ...,\n border : int | None = ...,\n table_id : str | None = ...,\n render_links : bool = ...,\n encoding : str | None = ...\n) -> str: ...", "kind": 2, "label": "to_html", "sortText": "0z.9999.to_html"}, {"detail": "\n@overload\ndef to_json(\n self : DataFrame,\n path_or_buf : PathLike[str] | WriteBuffer[str] | str,\n *,\n orient : Literal['records'],\n date_format : Literal['epoch', 'iso'] | None = ...,\n double_precision: int = ...,\n force_ascii : bool = ...,\n date_unit : TimeUnit = ...,\n default_handler : ((Any) -> JSONSerializable) | None = ...,\n lines : Literal[True],\n compression : CompressionOptions = ...,\n index : bool | None = ...,\n indent : int | None = ...,\n storage_options : dict[Unknown, Unknown] | None = ...,\n mode : Literal['a']\n) -> None: ...\ndef to_json(\n self : DataFrame,\n path_or_buf : None = None,\n *,\n orient : Literal['records'],\n date_format : Literal['epoch', 'iso'] | None = ...,\n double_precision: int = ...,\n force_ascii : bool = ...,\n date_unit : TimeUnit = ...,\n default_handler : ((Any) -> JSONSerializable) | None = ...,\n lines : Literal[True],\n compression : CompressionOptions = ...,\n index : bool | None = ...,\n indent : int | None = ...,\n storage_options : dict[Unknown, Unknown] | None = ...,\n mode : Literal['a']\n) -> str: ...\ndef to_json(\n self : DataFrame,\n path_or_buf : None = None,\n *,\n orient : Literal['columns', 'index', 'records', 'split', 'table', 'values'] | None = ...,\n date_format : Literal['epoch', 'iso'] | None = ...,\n double_precision: int = ...,\n force_ascii : bool = ...,\n date_unit : TimeUnit = ...,\n default_handler : ((Any) -> JSONSerializable) | None = ...,\n lines : bool = ...,\n compression : CompressionOptions = ...,\n index : bool | None = ...,\n indent : int | None = ...,\n storage_options : dict[Unknown, Unknown] | None = ...,\n mode : Literal['w'] = ...\n) -> str: ...\ndef to_json(\n self : DataFrame,\n path_or_buf : PathLike[str] | WriteBuffer[bytes] | WriteBuffer[str] | str,\n *,\n orient : Literal['columns', 'index', 'records', 'split', 'table', 'values'] | None = ...,\n date_format : Literal['epoch', 'iso'] | None = ...,\n double_precision: int = ...,\n force_ascii : bool = ...,\n date_unit : TimeUnit = ...,\n default_handler : ((Any) -> JSONSerializable) | None = ...,\n lines : bool = ...,\n compression : CompressionOptions = ...,\n index : bool | None = ...,\n indent : int | None = ...,\n storage_options : dict[Unknown, Unknown] | None = ...,\n mode : Literal['w'] = ...\n) -> None: ...", "kind": 2, "label": "to_json", "sortText": "0z.9999.to_json"}, {"detail": "\n@overload\ndef to_latex(\n self : DataFrame,\n buf : PathLike[str] | WriteBuffer[str] | str,\n columns : list[str] | None = ...,\n header : bool | list[str] = ...,\n index : bool = ...,\n na_rep : str = ...,\n formatters : Unknown = ...,\n float_format : Unknown = ...,\n sparsify : bool | None = ...,\n index_names : bool = ...,\n bold_rows : bool = ...,\n column_format : str | None = ...,\n longtable : bool | None = ...,\n escape : bool | None = ...,\n encoding : str | None = ...,\n decimal : str = ...,\n multicolumn : bool | None = ...,\n multicolumn_format: str | None = ...,\n multirow : bool | None = ...,\n caption : str | tuple[str, str] | None = ...,\n label : str | None = ...,\n position : str | None = ...\n) -> None: ...\ndef to_latex(\n self : DataFrame,\n buf : None = None,\n columns : list[str] | None = ...,\n header : bool | list[str] = ...,\n index : bool = ...,\n na_rep : str = ...,\n formatters : Unknown = ...,\n float_format : Unknown = ...,\n sparsify : bool | None = ...,\n index_names : bool = ...,\n bold_rows : bool = ...,\n column_format : str | None = ...,\n longtable : bool | None = ...,\n escape : bool | None = ...,\n encoding : str | None = ...,\n decimal : str = ...,\n multicolumn : bool | None = ...,\n multicolumn_format: str | None = ...,\n multirow : bool | None = ...,\n caption : str | tuple[str, str] | None = ...,\n label : str | None = ...,\n position : str | None = ...\n) -> str: ...", "kind": 2, "label": "to_latex", "sortText": "0z.9999.to_latex"}, {"detail": "\n@overload\ndef to_markdown(\n self : DataFrame,\n buf : PathLike[str] | WriteBuffer[str] | str,\n *,\n mode : FileWriteMode = ...,\n index : bool = ...,\n storage_options: StorageOptions = ...,\n **kwargs : Any\n) -> None: ...\ndef to_markdown(\n self : DataFrame,\n buf : None = None,\n *,\n mode : Literal['a', 'a+', 'a+b', 'ab', 'at', 'w', 'w+', 'w+b', 'wb', 'wt', 'x', 'xb', 'xt'] | None = ...,\n index : bool = ...,\n storage_options: StorageOptions = ...,\n **kwargs : Any\n) -> str: ...", "kind": 2, "label": "to_markdown", "sortText": "0z.9999.to_markdown"}, {"detail": "def to_numpy(\n self : DataFrame,\n dtype : _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = ...,\n copy : bool = False,\n na_value: Scalar = ...\n) -> ndarray[tuple[int, int]]: ...", "kind": 2, "label": "to_numpy", "sortText": "0z.9999.to_numpy"}, {"detail": "\n@overload\ndef to_orc(\n self : DataFrame,\n path : PathLike[str] | WriteBuffer[bytes] | str,\n *,\n engine : Literal['pyarrow'] = ...,\n index : bool | None = ...,\n engine_kwargs: dict[str, Any] | None = ...\n) -> None: ...\ndef to_orc(\n self : DataFrame,\n path : None = None,\n *,\n engine : Literal['pyarrow'] = ...,\n index : bool | None = ...,\n engine_kwargs: dict[str, Any] | None = ...\n) -> bytes: ...", "kind": 2, "label": "to_orc", "sortText": "0z.9999.to_orc"}, {"detail": "\n@overload\ndef to_parquet(\n self : DataFrame,\n path : PathLike[str] | WriteBuffer[bytes] | str,\n *,\n engine : ParquetEngine = ...,\n compression : Literal['brotli', 'gzip', 'lz4', 'snappy', 'zstd'] | None = ...,\n index : bool | None = ...,\n partition_cols : Sequence[Hashable] | None = ...,\n storage_options: StorageOptions = ...,\n **kwargs : Any\n) -> None: ...\ndef to_parquet(\n self : DataFrame,\n path : None = None,\n *,\n engine : ParquetEngine = ...,\n compression : Literal['brotli', 'gzip', 'lz4', 'snappy', 'zstd'] | None = ...,\n index : bool | None = ...,\n partition_cols : Sequence[Hashable] | None = ...,\n storage_options: StorageOptions = ...,\n **kwargs : Any\n) -> bytes: ...", "kind": 2, "label": "to_parquet", "sortText": "0z.9999.to_parquet"}, {"detail": "def to_period(\n self: DataFrame,\n freq: Day | Hour | Micro | Milli | Minute | MonthEnd | Nano | QuarterEnd | Second | Week | YearEnd | str | None = None,\n axis: Axis = 0,\n copy: bool = True\n) -> DataFrame: ...", "kind": 2, "label": "to_period", "sortText": "0z.9999.to_period"}, {"detail": "def to_pickle(\n self : DataFrame,\n path : PathLike[str] | WriteBuffer[bytes] | str,\n compression : CompressionOptions = 'infer',\n protocol : int = 5,\n storage_options: StorageOptions = ...\n) -> None: ...", "kind": 2, "label": "to_pickle", "sortText": "0z.9999.to_pickle"}, {"detail": "def to_records[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n index : bool = True,\n column_dtypes: Mapping[HashableT1, DTypeLike] | _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None,\n index_dtypes : Mapping[HashableT2, DTypeLike] | _HasDType[dtype] | _HasNumPyDType[dtype] | dtype | list[Any] | str | _DTypeDict | tuple[Any, Any] | type[Any] | None = None\n) -> recarray: ...", "kind": 2, "label": "to_records", "sortText": "0z.9999.to_records"}, {"detail": "def to_sql(\n self: DataFrame,\n name: str,\n con: Unknown,\n *,\n schema: str | None = None,\n if_exists: Literal['append', 'delete_rows', 'fail', 'replace'] = 'fail',\n index: bool = True,\n index_label: IndexLabel = None,\n chunksize: int | None = None,\n dtype: ExtensionDtype | Mapping[Hashable, ExtensionDtype | dtype[generic] | str | type[bool | complex | object | str]] | dtype[generic] | str | type[bool | complex | object | str] | None = None,\n method: Literal['multi'] | ((\n SQLTable,\n Any,\n list[str],\n Iterable[tuple[Any, ...]]\n) -> int | None) | None = None\n) -> int | None: ...", "kind": 2, "label": "to_sql", "sortText": "0z.9999.to_sql"}, {"detail": "\n@overload\ndef to_stata[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n path : PathLike[str] | WriteBuffer[bytes] | str,\n *,\n convert_dates : dict[HashableT1, StataDateFormat] | None = ...,\n write_index : bool = ...,\n byteorder : Literal['<', '>', 'big', 'little'] | None = ...,\n time_stamp : datetime | None = ...,\n data_label : str | None = ...,\n variable_labels: dict[HashableT2, str] | None = ...,\n version : Literal[117, 118, 119],\n convert_strl : SequenceNotStr[Hashable] | None = ...,\n compression : CompressionOptions = ...,\n storage_options: StorageOptions = ...,\n value_labels : dict[Hashable, dict[float, str]] | None = ...\n) -> None: ...\ndef to_stata[HashableT1: Hashable, HashableT2: Hashable](\n self : DataFrame,\n path : PathLike[str] | WriteBuffer[bytes] | str,\n *,\n convert_dates : dict[HashableT1, StataDateFormat] | None = ...,\n write_index : bool = ...,\n byteorder : Literal['<', '>', 'big', 'little'] | None = ...,\n time_stamp : datetime | None = ...,\n data_label : str | None = ...,\n variable_labels: dict[HashableT2, str] | None = ...,\n version : Literal[114, 117, 118, 119] | None = ...,\n convert_strl : None = None,\n compression : CompressionOptions = ...,\n storage_options: StorageOptions = ...,\n value_labels : dict[Hashable, dict[float, str]] | None = ...\n) -> None: ...", "kind": 2, "label": "to_stata", "sortText": "0z.9999.to_stata"}, {"detail": "\n@overload\ndef to_string[HashableT: Hashable](\n self : DataFrame,\n buf : PathLike[str] | WriteBuffer[str] | str,\n *,\n columns : Index | SequenceNotStr[Hashable] | Series | None = ...,\n col_space : dict[HashableT, int] | int | list[int] | None = ...,\n header : bool | list[str] | tuple[str, ...] = ...,\n index : bool = ...,\n na_rep : str = ...,\n formatters : Mapping[int | str, (...) -> Unknown] | list[(...) -> Unknown] | tuple[(...) -> Unknown, ...] | None = ...,\n float_format : ((float) -> str) | EngFormatter | str | None = ...,\n sparsify : bool | None = ...,\n index_names : bool = ...,\n justify : str | None = ...,\n max_rows : int | None = ...,\n max_cols : int | None = ...,\n show_dimensions: bool = ...,\n decimal : str = ...,\n line_width : int | None = ...,\n min_rows : int | None = ...,\n max_colwidth : int | None = ...,\n encoding : str | None = ...\n) -> None: ...\ndef to_string(\n self : DataFrame,\n buf : None = None,\n *,\n columns : Index | Sequence[Hashable] | Series | None = ...,\n col_space : dict[Hashable, int] | int | list[int] | None = ...,\n header : Sequence[str] | bool = ...,\n index : bool = ...,\n na_rep : str = ...,\n formatters : Mapping[int | str, (...) -> Unknown] | list[(...) -> Unknown] | tuple[(...) -> Unknown, ...] | None = ...,\n float_format : ((float) -> str) | EngFormatter | str | None = ...,\n sparsify : bool | None = ...,\n index_names : bool = ...,\n justify : str | None = ...,\n max_rows : int | None = ...,\n max_cols : int | None = ...,\n show_dimensions: bool = ...,\n decimal : str = ...,\n line_width : int | None = ...,\n min_rows : int | None = ...,\n max_colwidth : int | None = ...,\n encoding : str | None = ...\n) -> str: ...", "kind": 2, "label": "to_string", "sortText": "0z.9999.to_string"}, {"detail": "def to_timestamp(\n self: DataFrame,\n freq: Day | Hour | Micro | Milli | Minute | MonthEnd | Nano | QuarterEnd | Second | Week | YearEnd | str | None = None,\n how : ToTimestampHow = ...,\n axis: Axis = 0,\n copy: bool = True\n) -> DataFrame: ...", "kind": 2, "label": "to_timestamp", "sortText": "0z.9999.to_timestamp"}, {"detail": "def to_xarray(self: DataFrame) -> Unknown: ...", "kind": 2, "label": "to_xarray", "sortText": "0z.9999.to_xarray"}, {"detail": "\n@overload\ndef to_xml(\n self : DataFrame,\n path_or_buffer : PathLike[str] | WriteBuffer[bytes] | WriteBuffer[str] | str,\n index : bool = ...,\n root_name : str = ...,\n row_name : str = ...,\n na_rep : str | None = ...,\n attr_cols : SequenceNotStr[Hashable] | None = ...,\n elem_cols : SequenceNotStr[Hashable] | None = ...,\n namespaces : dict[str | None, str] | None = ...,\n prefix : str | None = ...,\n encoding : str = ...,\n xml_declaration: bool = ...,\n pretty_print : bool = ...,\n parser : XMLParsers = ...,\n stylesheet : PathLike[str] | ReadBuffer[bytes] | ReadBuffer[str] | str | None = ...,\n compression : CompressionOptions = ...,\n storage_options: StorageOptions = ...\n) -> None: ...\ndef to_xml(\n self : DataFrame,\n path_or_buffer : None = None,\n index : bool = ...,\n root_name : str | None = ...,\n row_name : str | None = ...,\n na_rep : str | None = ...,\n attr_cols : list[Hashable] | None = ...,\n elem_cols : list[Hashable] | None = ...,\n namespaces : dict[str | None, str] | None = ...,\n prefix : str | None = ...,\n encoding : str = ...,\n xml_declaration: bool | None = ...,\n pretty_print : bool | None = ...,\n parser : str | None = ...,\n stylesheet : PathLike[str] | ReadBuffer[bytes] | ReadBuffer[str] | str | None = ...,\n compression : CompressionOptions = ...,\n storage_options: StorageOptions = ...\n) -> str: ...", "kind": 2, "label": "to_xml", "sortText": "0z.9999.to_xml"}, {"detail": "def transform(\n self : DataFrame,\n func : AggFuncTypeFrame,\n axis : Axis = 0,\n *args : Any,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "transform", "sortText": "0z.9999.transform"}, {"detail": "def transpose(\n self : DataFrame,\n *args: Any,\n *,\n copy : bool = ...\n) -> DataFrame: ...", "kind": 2, "label": "transpose", "sortText": "0z.9999.transpose"}, {"detail": "def truediv(\n self : DataFrame,\n other : DataFrame | ExtensionArray | Index | SequenceNotStr[Any] | Series | complex | ndarray | range,\n axis : Literal['columns', 'index', 0, 1] | None = 'columns',\n level : Hashable | None = None,\n fill_value: float | None = None\n) -> DataFrame: ...", "kind": 2, "label": "truediv", "sortText": "0z.9999.truediv"}, {"detail": "def truncate(\n self : DataFrame,\n before: date | int | str | None = ...,\n after : date | int | str | None = ...,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n copy : bool = ...\n) -> DataFrame: ...", "kind": 2, "label": "truncate", "sortText": "0z.9999.truncate"}, {"detail": "def tz_convert(\n self : DataFrame,\n tz : TimeZones,\n axis : Axis = 0,\n level: Hashable | None = None,\n copy : bool = True\n) -> DataFrame: ...", "kind": 2, "label": "tz_convert", "sortText": "0z.9999.tz_convert"}, {"detail": "def tz_localize(\n self : DataFrame,\n tz : TimeZones,\n axis : Axis = 0,\n level : Hashable | None = None,\n copy : builtins.bool = True,\n ambiguous : TimeAmbiguous = 'raise',\n nonexistent: TimeNonexistent = 'raise'\n) -> DataFrame: ...", "kind": 2, "label": "tz_localize", "sortText": "0z.9999.tz_localize"}, {"detail": "def unstack(\n self : DataFrame,\n level : IndexLabel = -1,\n fill_value: Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | None = None,\n sort : bool = True\n) -> DataFrame | Series: ...", "kind": 2, "label": "unstack", "sortText": "0z.9999.unstack"}, {"detail": "def update(\n self : DataFrame,\n other : DataFrame | Series,\n join : Literal['left'] = 'left',\n overwrite : bool = True,\n filter_func: ((...) -> Unknown) | None = ...,\n errors : IgnoreRaise = 'ignore'\n) -> None: ...", "kind": 2, "label": "update", "sortText": "0z.9999.update"}, {"detail": "\n@overload\ndef value_counts(\n self : DataFrame,\n subset : Sequence[Hashable] | None = ...,\n normalize: Literal[False] = False,\n sort : bool = ...,\n ascending: bool = ...,\n dropna : bool = ...\n) -> Series[int]: ...\ndef value_counts(\n self : DataFrame,\n normalize: Literal[True],\n subset : Sequence[Hashable] | None = ...,\n sort : bool = ...,\n ascending: bool = ...,\n dropna : bool = ...\n) -> Series[float]: ...", "kind": 2, "label": "value_counts", "sortText": "0z.9999.value_counts"}, {"detail": "ndarray[tuple[int, int]]", "kind": 5, "label": "values", "sortText": "0z.9999.values"}, {"detail": "def var(\n self : DataFrame,\n axis : Literal['columns', 'index', 0, 1] | None = 0,\n skipna : bool | None = True,\n ddof : int = 1,\n numeric_only: bool = False,\n **kwargs : Any\n) -> Series: ...", "kind": 2, "label": "var", "sortText": "0z.9999.var"}, {"detail": "\n@overload\ndef where(\n self : DataFrame,\n cond : ((DataFrame) -> DataFrame) | ((Any) -> builtins.bool) | DataFrame | Series | ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other : ((...) -> DataFrame | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64) | DataFrame | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 = ...,\n *,\n inplace: Literal[True],\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n level : Hashable | None = None\n) -> None: ...\ndef where(\n self : DataFrame,\n cond : ((DataFrame) -> DataFrame) | ((Any) -> builtins.bool) | DataFrame | Series | ndarray[tuple[Any, ...], dtype[numpy.bool]],\n other : ((...) -> DataFrame | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64) | DataFrame | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 = ...,\n *,\n inplace: Literal[False] = False,\n axis : Literal['columns', 'index', 0, 1] | None = ...,\n level : Hashable | None = None\n) -> DataFrame: ...", "kind": 2, "label": "where", "sortText": "0z.9999.where"}, {"detail": "def xs(\n self : DataFrame,\n key : IndexLabel,\n axis : Axis = 0,\n level : Hashable | Sequence[Hashable] | None = None,\n drop_level: bool = True\n) -> DataFrame | Series: ...", "kind": 2, "label": "xs", "sortText": "0z.9999.xs"}, {"detail": "def __abs__(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "__abs__", "sortText": "3z.9999.__abs__"}, {"detail": "def __add__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__add__", "sortText": "3z.9999.__add__"}, {"detail": "def __and__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__and__", "sortText": "3z.9999.__and__"}, {"detail": "def __array__(\n self : DataFrame,\n dtype: Unknown = ...\n) -> ndarray: ...", "kind": 2, "label": "__array__", "sortText": "3z.9999.__array__"}, {"detail": "int", "kind": 5, "label": "__array_priority__", "sortText": "3z.9999.__array_priority__"}, {"detail": "def __arrow_c_stream__(\n self : DataFrame,\n requested_schema: object | None = None\n) -> object: ...", "kind": 2, "label": "__arrow_c_stream__", "sortText": "3z.9999.__arrow_c_stream__"}, {"detail": "def __bool__(self: DataFrame) -> NoReturn: ...", "kind": 2, "label": "__bool__", "sortText": "3z.9999.__bool__"}, {"detail": "def __contains__(\n self: DataFrame,\n key: Unknown\n) -> bool: ...", "kind": 2, "label": "__contains__", "sortText": "3z.9999.__contains__"}, {"detail": "def __copy__(\n self: DataFrame,\n deep: bool = ...\n) -> DataFrame: ...", "kind": 2, "label": "__copy__", "sortText": "3z.9999.__copy__"}, {"detail": "def __deepcopy__(\n self: DataFrame,\n memo: MutableMapping[int, Any] | None = None\n) -> DataFrame: ...", "kind": 2, "label": "__deepcopy__", "sortText": "3z.9999.__deepcopy__"}, {"detail": "def __delitem__(\n self: DataFrame,\n idx: Hashable\n) -> None: ...", "kind": 2, "label": "__delitem__", "sortText": "3z.9999.__delitem__"}, {"detail": "def __divmod__(\n self: DataFrame,\n other: Any\n) -> tuple[DataFrame, DataFrame]: ...", "kind": 2, "label": "__divmod__", "sortText": "3z.9999.__divmod__"}, {"detail": "def __eq__(\n self: DataFrame,\n other: object\n) -> DataFrame: ...", "kind": 2, "label": "__eq__", "sortText": "3z.9999.__eq__"}, {"detail": "def __finalize__(\n self : DataFrame,\n other : Unknown,\n method : Unknown = ...,\n **kwargs: Any\n) -> DataFrame: ...", "kind": 2, "label": "__finalize__", "sortText": "3z.9999.__finalize__"}, {"detail": "def __floordiv__(\n self: DataFrame,\n other: DataFrame | Sequence[float] | Series[float] | Series[int] | float\n) -> DataFrame: ...", "kind": 2, "label": "__floordiv__", "sortText": "3z.9999.__floordiv__"}, {"detail": "def __ge__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__ge__", "sortText": "3z.9999.__ge__"}, {"detail": "def __getattr__(\n self: DataFrame,\n name: str\n) -> Series: ...", "kind": 2, "label": "__getattr__", "sortText": "3z.9999.__getattr__"}, {"detail": "\n@overload\ndef __getitem__(\n self: DataFrame,\n key: Timedelta | Timestamp | bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64 | tuple[Hashable, ...]\n) -> Series: ...\ndef __getitem__(\n self: DataFrame,\n key: Iterable[Hashable] | slice[Any, Any, Any]\n) -> DataFrame: ...\ndef __getitem__(\n self: DataFrame,\n key: Hashable\n) -> Series: ...", "kind": 2, "label": "__getitem__", "sortText": "3z.9999.__getitem__"}, {"detail": "def __gt__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__gt__", "sortText": "3z.9999.__gt__"}, {"detail": "None", "kind": 5, "label": "__hash__", "sortText": "3z.9999.__hash__"}, {"detail": "def __invert__(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "__invert__", "sortText": "3z.9999.__invert__"}, {"detail": "def __iter__(self: DataFrame) -> Iterator[Hashable]: ...", "kind": 2, "label": "__iter__", "sortText": "3z.9999.__iter__"}, {"detail": "def __le__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__le__", "sortText": "3z.9999.__le__"}, {"detail": "def __len__(self: DataFrame) -> int: ...", "kind": 2, "label": "__len__", "sortText": "3z.9999.__len__"}, {"detail": "def __lt__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__lt__", "sortText": "3z.9999.__lt__"}, {"detail": "\n@overload\ndef __matmul__(\n self: DataFrame,\n other: DataFrame\n) -> DataFrame: ...\ndef __matmul__(\n self: DataFrame,\n other: Series\n) -> Series: ...\ndef __matmul__(\n self: DataFrame,\n other: ndarray[tuple[Any, ...], dtype[np_num]]\n) -> DataFrame: ...", "kind": 2, "label": "__matmul__", "sortText": "3z.9999.__matmul__"}, {"detail": "def __mod__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__mod__", "sortText": "3z.9999.__mod__"}, {"detail": "def __mul__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__mul__", "sortText": "3z.9999.__mul__"}, {"detail": "def __ne__(\n self: DataFrame,\n other: object\n) -> DataFrame: ...", "kind": 2, "label": "__ne__", "sortText": "3z.9999.__ne__"}, {"detail": "def __neg__(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "__neg__", "sortText": "3z.9999.__neg__"}, {"detail": "\n@overload\ndef __new__[Self@DataFrame: DataFrame](\n cls : type[Self@DataFrame],\n data : DataFrame | Index | Iterable[Index | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | tuple[Hashable, ListLikeU]] | Sequence[Unknown] | Series | dict[Any, Any] | ndarray[tuple[int]] | None = None,\n index : ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n columns: ExtensionArray | Index | SequenceNotStr[Any] | Series | ndarray | range | None = None,\n dtype : ExtensionDtype | dtype[generic] | str | type[bool | complex | object | str] | None = None,\n copy : bool | None = None\n) -> Self@DataFrame: ...\ndef __new__[Self@DataFrame: DataFrame](\n cls : type[Self@DataFrame],\n data : Scalar,\n index : Axes,\n columns: Axes,\n dtype : ExtensionDtype | dtype[generic] | str | type[bool | complex | object | str] | None = None,\n copy : bool | None = None\n) -> Self@DataFrame: ...", "kind": 3, "label": "__new__", "sortText": "3z.9999.__new__"}, {"detail": "def __nonzero__(self: DataFrame) -> None: ...", "kind": 2, "label": "__nonzero__", "sortText": "3z.9999.__nonzero__"}, {"detail": "def __or__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__or__", "sortText": "3z.9999.__or__"}, {"detail": "def __pos__(self: DataFrame) -> DataFrame: ...", "kind": 2, "label": "__pos__", "sortText": "3z.9999.__pos__"}, {"detail": "def __pow__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__pow__", "sortText": "3z.9999.__pow__"}, {"detail": "def __radd__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__radd__", "sortText": "3z.9999.__radd__"}, {"detail": "def __rand__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__rand__", "sortText": "3z.9999.__rand__"}, {"detail": "def __rdivmod__(\n self: DataFrame,\n other: Any\n) -> tuple[DataFrame, DataFrame]: ...", "kind": 2, "label": "__rdivmod__", "sortText": "3z.9999.__rdivmod__"}, {"detail": "def __rfloordiv__(\n self: DataFrame,\n other: DataFrame | Sequence[float] | Series[float] | Series[int] | float\n) -> DataFrame: ...", "kind": 2, "label": "__rfloordiv__", "sortText": "3z.9999.__rfloordiv__"}, {"detail": "def __rmatmul__(\n self: DataFrame,\n other: ndarray[tuple[Any, ...], dtype[np_num]]\n) -> DataFrame: ...", "kind": 2, "label": "__rmatmul__", "sortText": "3z.9999.__rmatmul__"}, {"detail": "def __rmod__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__rmod__", "sortText": "3z.9999.__rmod__"}, {"detail": "def __rmul__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__rmul__", "sortText": "3z.9999.__rmul__"}, {"detail": "def __ror__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__ror__", "sortText": "3z.9999.__ror__"}, {"detail": "def __round__(\n self : DataFrame,\n decimals: int = ...\n) -> DataFrame: ...", "kind": 2, "label": "__round__", "sortText": "3z.9999.__round__"}, {"detail": "def __rpow__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__rpow__", "sortText": "3z.9999.__rpow__"}, {"detail": "def __rsub__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__rsub__", "sortText": "3z.9999.__rsub__"}, {"detail": "def __rtruediv__(\n self: DataFrame,\n other: DataFrame | Sequence[Unknown] | Series | float\n) -> DataFrame: ...", "kind": 2, "label": "__rtruediv__", "sortText": "3z.9999.__rtruediv__"}, {"detail": "def __rxor__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__rxor__", "sortText": "3z.9999.__rxor__"}, {"detail": "def __setattr__(\n self: DataFrame,\n name: str,\n value: Unknown\n) -> None: ...", "kind": 2, "label": "__setattr__", "sortText": "3z.9999.__setattr__"}, {"detail": "\n@overload\ndef __setitem__(\n self: DataFrame,\n idx: Index | Series[int] | int | list[int] | ndarray[tuple[Any, ...], dtype[integer]] | slice[Any, Any, Any] | tuple[IndexType, IndexType] | tuple[IndexType, int] | tuple[int, IndexType] | tuple[int, int],\n value: DataFrame | IndexOpsMixin | Mapping[Hashable, NAType | NaTType | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | NAType | NaTType | Sequence[Scalar] | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64 | None\n) -> None: ...\ndef __setitem__[ScalarT: Scalar](\n self: DataFrame,\n idx: Series[builtins.bool] | int | integer | list[builtins.bool] | list[ScalarT] | ndarray[tuple[Any, ...], dtype[numpy.bool]] | signedinteger[_NBitIntP] | signedinteger[_8Bit] | signedinteger | slice[Any, Any, Any] | str | str_ | unsignedinteger | tuple[IndexOpsMixin | Sequence[Scalar] | Series[builtins.bool] | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | list[builtins.bool] | ndarray[tuple[Any, ...], dtype[numpy.bool]] | slice[Any, Any, Any] | str | timedelta | timedelta64, ...],\n value: DataFrame | ExtensionArray | IndexOpsMixin | Mapping[Hashable, NAType | NaTType | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | NAType | NaTType | Sequence[Scalar] | Sequence[Sequence[Scalar]] | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64 | None\n) -> None: ...\ndef __setitem__(\n self: DataFrame,\n idx: tuple[tuple[IndexOpsMixin | Sequence[Scalar] | Series[builtins.bool] | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | list[builtins.bool] | ndarray[tuple[Any, ...], dtype[numpy.bool]] | slice[Any, Any, Any] | str | timedelta | timedelta64, ...], Hashable],\n value: ExtensionArray | IndexOpsMixin | Mapping[Hashable, NAType | NaTType | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | NAType | NaTType | Sequence[Scalar] | Sequence[Sequence[Scalar]] | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64 | None\n) -> None: ...\ndef __setitem__(\n self: DataFrame,\n idx: DataFrame | IndexOpsMixin,\n value: ExtensionArray | IndexOpsMixin | Mapping[Hashable, NAType | NaTType | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | str | timedelta | timedelta64] | NAType | NaTType | Sequence[Scalar] | Sequence[Sequence[Scalar]] | Timedelta | Timestamp | builtins.bool | bytes | complex | complexfloating | date | datetime | datetime64 | float | floating | int | integer | ndarray | str | timedelta | timedelta64 | None\n) -> None: ...", "kind": 2, "label": "__setitem__", "sortText": "3z.9999.__setitem__"}, {"detail": "def __sub__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__sub__", "sortText": "3z.9999.__sub__"}, {"detail": "def __truediv__(\n self: DataFrame,\n other: DataFrame | Sequence[Unknown] | Series | float\n) -> DataFrame: ...", "kind": 2, "label": "__truediv__", "sortText": "3z.9999.__truediv__"}, {"detail": "def __xor__(\n self: DataFrame,\n other: Any\n) -> DataFrame: ...", "kind": 2, "label": "__xor__", "sortText": "3z.9999.__xor__"}]}} -{"suite": "pandas", "label": "edit dataframe then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 17, "character": 20, "iteration": 1, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) drop: def drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable] = ...,\n columns: Hashable | Iterable[Hashable],\n level : Hashable | None = ...,\n inplace: Literal[False] = False,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/frame.pyi#L396,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L555,7) | [Index](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L566,7)"}}} -{"suite": "pandas", "label": "edit dataframe then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 17, "character": 20, "iteration": 2, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) drop: def drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable] = ...,\n columns: Hashable | Iterable[Hashable],\n level : Hashable | None = ...,\n inplace: Literal[False] = False,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/frame.pyi#L396,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L555,7) | [Index](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L566,7)"}}} -{"suite": "pandas", "label": "edit dataframe then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 17, "character": 20, "iteration": 3, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) drop: def drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable] = ...,\n columns: Hashable | Iterable[Hashable],\n level : Hashable | None = ...,\n inplace: Literal[False] = False,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/frame.pyi#L396,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L555,7) | [Index](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L566,7)"}}} -{"suite": "pandas", "label": "edit dataframe then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 17, "character": 20, "iteration": 4, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) drop: def drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable] = ...,\n columns: Hashable | Iterable[Hashable],\n level : Hashable | None = ...,\n inplace: Literal[False] = False,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/frame.pyi#L396,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L555,7) | [Index](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L566,7)"}}} -{"suite": "pandas", "label": "edit dataframe then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 17, "character": 20, "iteration": 5, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) drop: def drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable] = ...,\n columns: Hashable | Iterable[Hashable],\n level : Hashable | None = ...,\n inplace: Literal[False] = False,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/frame.pyi#L396,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L555,7) | [Index](file:///tmp/pyrefly_bundled_third_party_8e683b98e45e/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L566,7)"}}} +{"suite": "pandas", "label": "edit dataframe then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 17, "character": 20, "iteration": 1, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) drop: def drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable] = ...,\n columns: Hashable | Iterable[Hashable],\n level : Hashable | None = ...,\n inplace: Literal[False] = False,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/frame.pyi#L396,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L555,7) | [Index](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L566,7)"}}} +{"suite": "pandas", "label": "edit dataframe then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 17, "character": 20, "iteration": 2, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) drop: def drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable] = ...,\n columns: Hashable | Iterable[Hashable],\n level : Hashable | None = ...,\n inplace: Literal[False] = False,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/frame.pyi#L396,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L555,7) | [Index](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L566,7)"}}} +{"suite": "pandas", "label": "edit dataframe then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 17, "character": 20, "iteration": 3, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) drop: def drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable] = ...,\n columns: Hashable | Iterable[Hashable],\n level : Hashable | None = ...,\n inplace: Literal[False] = False,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/frame.pyi#L396,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L555,7) | [Index](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L566,7)"}}} +{"suite": "pandas", "label": "edit dataframe then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 17, "character": 20, "iteration": 4, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) drop: def drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable] = ...,\n columns: Hashable | Iterable[Hashable],\n level : Hashable | None = ...,\n inplace: Literal[False] = False,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/frame.pyi#L396,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L555,7) | [Index](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L566,7)"}}} +{"suite": "pandas", "label": "edit dataframe then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/pandas/src/report.py", "line": 17, "character": 20, "iteration": 5, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) drop: def drop(\n self : DataFrame,\n labels : None = None,\n *,\n axis : Axis = ...,\n index : Hashable | Index | Sequence[Hashable] = ...,\n columns: Hashable | Iterable[Hashable],\n level : Hashable | None = ...,\n inplace: Literal[False] = False,\n errors : IgnoreRaise = ...\n) -> DataFrame: ...\n```\n\nGo to [DataFrame](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/frame.pyi#L396,7) | [Hashable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L555,7) | [Index](file:///tmp/pyrefly_bundled_third_party_d8d89b9c22f8/pandas-stubs/core/indexes/base.pyi#L144,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L566,7)"}}} {"suite": "sqlalchemy", "label": "query completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 16, "character": 22, "iteration": 1, "result": {"isIncomplete": true, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"detail": "sessionmaker[Session]", "kind": 6, "label": "SessionLocal", "sortText": "0.9999.SessionLocal"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}]}} {"suite": "sqlalchemy", "label": "query completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 16, "character": 22, "iteration": 2, "result": {"isIncomplete": true, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"detail": "sessionmaker[Session]", "kind": 6, "label": "SessionLocal", "sortText": "0.9999.SessionLocal"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}]}} {"suite": "sqlalchemy", "label": "query completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 16, "character": 22, "iteration": 3, "result": {"isIncomplete": true, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"detail": "sessionmaker[Session]", "kind": 6, "label": "SessionLocal", "sortText": "0.9999.SessionLocal"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}]}} {"suite": "sqlalchemy", "label": "query completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 16, "character": 22, "iteration": 4, "result": {"isIncomplete": true, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"detail": "sessionmaker[Session]", "kind": 6, "label": "SessionLocal", "sortText": "0.9999.SessionLocal"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}]}} {"suite": "sqlalchemy", "label": "query completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 16, "character": 22, "iteration": 5, "result": {"isIncomplete": true, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"detail": "sessionmaker[Session]", "kind": 6, "label": "SessionLocal", "sortText": "0.9999.SessionLocal"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}]}} -{"suite": "sqlalchemy", "label": "sessionmaker hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 12, "character": 27, "iteration": 1, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(function) mapped_column: def mapped_column[_T](\n __name_pos : SchemaEventTarget | TypeEngine[Any] | str | type[TypeEngine[Any]] | None = None,\n __type_pos : SchemaEventTarget | TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n /,\n *args : SchemaEventTarget,\n *,\n init : _NoArg | bool = _NoArg.NO_ARG,\n repr : _NoArg | bool = _NoArg.NO_ARG,\n default : Any | None = _NoArg.NO_ARG,\n default_factory : (() -> _T) | _NoArg = _NoArg.NO_ARG,\n compare : _NoArg | bool = _NoArg.NO_ARG,\n kw_only : _NoArg | bool = _NoArg.NO_ARG,\n hash : _NoArg | bool | None = _NoArg.NO_ARG,\n nullable : Literal[SchemaConst.NULL_UNSPECIFIED] | bool | None = SchemaConst.NULL_UNSPECIFIED,\n primary_key : bool | None = False,\n deferred : _NoArg | bool = _NoArg.NO_ARG,\n deferred_group : str | None = None,\n deferred_raiseload : bool | None = None,\n use_existing_column: bool = False,\n name : str | None = None,\n type_ : TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n autoincrement : _AutoIncrementType = 'auto',\n doc : str | None = None,\n key : str | None = None,\n index : bool | None = None,\n unique : bool | None = None,\n info : dict[Any, Any] | None = None,\n onupdate : Any | None = None,\n insert_default : Any | None = _NoArg.NO_ARG,\n server_default : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n server_onupdate : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n active_history : bool = False,\n quote : bool | None = None,\n system : bool = False,\n comment : str | None = None,\n sort_order : _NoArg | int = _NoArg.NO_ARG,\n dataclass_metadata : Mapping[Any, Any] | _NoArg | None = _NoArg.NO_ARG,\n **kw : Any\n) -> MappedColumn[Any]: ...\n```\n---\ndeclare a new ORM-mapped `_schema.Column` construct \nfor use within `Declarative Table ` \nconfiguration. \n \nThe `_orm.mapped_column` function provides an ORM-aware and \nPython-typing-compatible construct which is used with \n`declarative ` mappings to indicate an \nattribute that's mapped to a Core `_schema.Column` object. It \nprovides the equivalent feature as mapping an attribute to a \n`_schema.Column` object directly when using Declarative, \nspecifically when using `Declarative Table ` \nconfiguration. \n \n.. versionadded:: 2.0 \n \n`_orm.mapped_column` is normally used with explicit typing along with \nthe `_orm.Mapped` annotation type, where it can derive the SQL \ntype and nullability for the column based on what's present within the \n`_orm.Mapped` annotation. It also may be used without annotations \nas a drop-in replacement for how `_schema.Column` is used in \nDeclarative mappings in SQLAlchemy 1.x style. \n \nFor usage examples of `_orm.mapped_column`, see the documentation \nat `orm_declarative_table`. \n \n.. seealso: \n \n``` \n`orm_declarative_table` - complete documentation \n \n`whatsnew_20_orm_declarative_typing` - migration notes for \nDeclarative mappings using 1.x style mappings \n \n``` \n:param __name: String name to give to the `_schema.Column`. This \n is an optional, positional only argument that if present must be the \n first positional argument passed. If omitted, the attribute name to \n which the `_orm.mapped_column` is mapped will be used as the SQL \n column name. \n:param __type: `_types.TypeEngine` type or instance which will \n indicate the datatype to be associated with the `_schema.Column`. \n This is an optional, positional-only argument that if present must \n immediately follow the ``__name`` parameter if present also, or otherwise \n be the first positional parameter. If omitted, the ultimate type for \n the column may be derived either from the annotated type, or if a \n `_schema.ForeignKey` is present, from the datatype of the \n referenced column. \n:param \\*args: Additional positional arguments include constructs such \n as `_schema.ForeignKey`, `_schema.CheckConstraint`, \n and `_schema.Identity`, which are passed through to the constructed \n `_schema.Column`. \n:param nullable: Optional bool, whether the column should be \"NULL\" or \n \"NOT NULL\". If omitted, the nullability is derived from the type \n annotation based on whether or not ``typing.Optional`` (or its equivalent) \n is present. ``nullable`` defaults to ``True`` otherwise for non-primary \n key columns, and ``False`` for primary key columns. \n:param primary_key: optional bool, indicates the `_schema.Column` \n would be part of the table's primary key or not. \n:param deferred: Optional bool - this keyword argument is consumed by the \n ORM declarative process, and is not part of the `_schema.Column` \n itself; instead, it indicates that this column should be \"deferred\" for \n loading as though mapped by `_orm.deferred`. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_declarative` \n \n``` \n:param deferred_group: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.group` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_group` \n \n``` \n:param deferred_raiseload: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.raiseload` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_raiseload` \n \n``` \n:param use_existing_column: if True, will attempt to locate the given \n column name on an inherited superclass (typically single inheriting \n superclass), and if present, will not produce a new column, mapping \n to the superclass column as though it were omitted from this class. \n This is used for mixins that add new columns to an inherited superclass. \n \n .. seealso: \n \n``` \n`orm_inheritance_column_conflicts` \n \n``` \n .. versionadded:: 2.0.0b4 \n \n:param default: Passed directly to the \n `_schema.Column.default` parameter if the \n `_orm.mapped_column.insert_default` parameter is not present. \n Additionally, when used with `orm_declarative_native_dataclasses`, \n indicates a default Python value that should be applied to the keyword \n constructor within the generated ``__init__()`` method. \n \n Note that in the case of dataclass generation when \n `_orm.mapped_column.insert_default` is not present, this means \n the `_orm.mapped_column.default` value is used in **two** \n places, both the ``__init__()`` method as well as the \n `_schema.Column.default` parameter. While this behavior may \n change in a future release, for the moment this tends to \"work out\"; a \n default of ``None`` will mean that the `_schema.Column` gets no \n default generator, whereas a default that refers to a non-``None`` Python \n or SQL expression value will be assigned up front on the object when \n ``__init__()`` is called, which is the same value that the Core \n `_sql.Insert` construct would use in any case, leading to the same \n end result. \n \n .. note:: When using Core level column defaults that are callables to \n    be interpreted by the underlying `_schema.Column` in conjunction \n    with `ORM-mapped dataclasses \n    `, especially those that are \n    `context-aware default functions `, \n    **the** `_orm.mapped_column.insert_default` **parameter must \n    be used instead**. This is necessary to disambiguate the callable from \n    being interpreted as a dataclass level default. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.insert_default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param insert_default: Passed directly to the \n `_schema.Column.default` parameter; will supersede the value \n of `_orm.mapped_column.default` when present, however \n `_orm.mapped_column.default` will always apply to the \n constructor default for a dataclasses mapping. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param sort_order: An integer that indicates how this mapped column \n should be sorted compared to the others when the ORM is creating a \n `_schema.Table`. Among mapped columns that have the same \n value the default ordering is used, placing first the mapped columns \n defined in the main class, then the ones in the super classes. \n Defaults to 0. The sort is ascending. \n \n .. versionadded:: 2.0.4 \n \n:param active_history=False: \n \n    When ``True``, indicates that the \"previous\" value for a \n    scalar attribute should be loaded when replaced, if not \n    already loaded. Normally, history tracking logic for \n    simple non-primary-key scalar values only needs to be \n    aware of the \"new\" value in order to perform a flush. This \n    flag is available for applications that make use of \n    `.attributes.get_history` or `.Session.is_modified` \n    which also need to know the \"previous\" value of the attribute. \n \n    .. versionadded:: 2.0.10 \n \n \n:param init: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__init__()`` \n method as generated by the dataclass process. \n:param repr: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__repr__()`` \n method as generated by the dataclass process. \n:param default_factory: Specific to \n `orm_declarative_native_dataclasses`, \n specifies a default-value generation function that will take place \n as part of the ``__init__()`` \n method as generated by the dataclass process. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.insert_default` \n \n``` \n:param compare: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be included in comparison operations when generating the \n ``__eq__()`` and ``__ne__()`` methods for the mapped class. \n \n .. versionadded:: 2.0.0b4 \n \n:param kw_only: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be marked as keyword-only when generating the ``__init__()``. \n \n:param hash: Specific to \n `orm_declarative_native_dataclasses`, controls if this field \n is included when generating the ``__hash__()`` method for the mapped \n class. \n \n .. versionadded:: 2.0.36 \n \n:param dataclass_metadata: Specific to \n `orm_declarative_native_dataclasses`, supplies metadata \n to be attached to the generated dataclass field. \n \n .. versionadded:: 2.0.42 \n \n:param \\**kw: All remaining keyword arguments are passed through to the \n constructor for the `_schema.Column`.\n\nGo to [SchemaEventTarget](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L1823,7) | [TypeEngine](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/type_api.py#L116,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [_NoArg](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L118,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [SchemaConst](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L145,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3627,7) | [ColumnElement](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L1287,7) | [FetchedValue](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L4706,7) | [TextClause](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L2545,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L862,7) | [MappedColumn](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/properties.py#L516,7)"}}} -{"suite": "sqlalchemy", "label": "sessionmaker hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 12, "character": 27, "iteration": 2, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(function) mapped_column: def mapped_column[_T](\n __name_pos : SchemaEventTarget | TypeEngine[Any] | str | type[TypeEngine[Any]] | None = None,\n __type_pos : SchemaEventTarget | TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n /,\n *args : SchemaEventTarget,\n *,\n init : _NoArg | bool = _NoArg.NO_ARG,\n repr : _NoArg | bool = _NoArg.NO_ARG,\n default : Any | None = _NoArg.NO_ARG,\n default_factory : (() -> _T) | _NoArg = _NoArg.NO_ARG,\n compare : _NoArg | bool = _NoArg.NO_ARG,\n kw_only : _NoArg | bool = _NoArg.NO_ARG,\n hash : _NoArg | bool | None = _NoArg.NO_ARG,\n nullable : Literal[SchemaConst.NULL_UNSPECIFIED] | bool | None = SchemaConst.NULL_UNSPECIFIED,\n primary_key : bool | None = False,\n deferred : _NoArg | bool = _NoArg.NO_ARG,\n deferred_group : str | None = None,\n deferred_raiseload : bool | None = None,\n use_existing_column: bool = False,\n name : str | None = None,\n type_ : TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n autoincrement : _AutoIncrementType = 'auto',\n doc : str | None = None,\n key : str | None = None,\n index : bool | None = None,\n unique : bool | None = None,\n info : dict[Any, Any] | None = None,\n onupdate : Any | None = None,\n insert_default : Any | None = _NoArg.NO_ARG,\n server_default : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n server_onupdate : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n active_history : bool = False,\n quote : bool | None = None,\n system : bool = False,\n comment : str | None = None,\n sort_order : _NoArg | int = _NoArg.NO_ARG,\n dataclass_metadata : Mapping[Any, Any] | _NoArg | None = _NoArg.NO_ARG,\n **kw : Any\n) -> MappedColumn[Any]: ...\n```\n---\ndeclare a new ORM-mapped `_schema.Column` construct \nfor use within `Declarative Table ` \nconfiguration. \n \nThe `_orm.mapped_column` function provides an ORM-aware and \nPython-typing-compatible construct which is used with \n`declarative ` mappings to indicate an \nattribute that's mapped to a Core `_schema.Column` object. It \nprovides the equivalent feature as mapping an attribute to a \n`_schema.Column` object directly when using Declarative, \nspecifically when using `Declarative Table ` \nconfiguration. \n \n.. versionadded:: 2.0 \n \n`_orm.mapped_column` is normally used with explicit typing along with \nthe `_orm.Mapped` annotation type, where it can derive the SQL \ntype and nullability for the column based on what's present within the \n`_orm.Mapped` annotation. It also may be used without annotations \nas a drop-in replacement for how `_schema.Column` is used in \nDeclarative mappings in SQLAlchemy 1.x style. \n \nFor usage examples of `_orm.mapped_column`, see the documentation \nat `orm_declarative_table`. \n \n.. seealso: \n \n``` \n`orm_declarative_table` - complete documentation \n \n`whatsnew_20_orm_declarative_typing` - migration notes for \nDeclarative mappings using 1.x style mappings \n \n``` \n:param __name: String name to give to the `_schema.Column`. This \n is an optional, positional only argument that if present must be the \n first positional argument passed. If omitted, the attribute name to \n which the `_orm.mapped_column` is mapped will be used as the SQL \n column name. \n:param __type: `_types.TypeEngine` type or instance which will \n indicate the datatype to be associated with the `_schema.Column`. \n This is an optional, positional-only argument that if present must \n immediately follow the ``__name`` parameter if present also, or otherwise \n be the first positional parameter. If omitted, the ultimate type for \n the column may be derived either from the annotated type, or if a \n `_schema.ForeignKey` is present, from the datatype of the \n referenced column. \n:param \\*args: Additional positional arguments include constructs such \n as `_schema.ForeignKey`, `_schema.CheckConstraint`, \n and `_schema.Identity`, which are passed through to the constructed \n `_schema.Column`. \n:param nullable: Optional bool, whether the column should be \"NULL\" or \n \"NOT NULL\". If omitted, the nullability is derived from the type \n annotation based on whether or not ``typing.Optional`` (or its equivalent) \n is present. ``nullable`` defaults to ``True`` otherwise for non-primary \n key columns, and ``False`` for primary key columns. \n:param primary_key: optional bool, indicates the `_schema.Column` \n would be part of the table's primary key or not. \n:param deferred: Optional bool - this keyword argument is consumed by the \n ORM declarative process, and is not part of the `_schema.Column` \n itself; instead, it indicates that this column should be \"deferred\" for \n loading as though mapped by `_orm.deferred`. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_declarative` \n \n``` \n:param deferred_group: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.group` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_group` \n \n``` \n:param deferred_raiseload: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.raiseload` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_raiseload` \n \n``` \n:param use_existing_column: if True, will attempt to locate the given \n column name on an inherited superclass (typically single inheriting \n superclass), and if present, will not produce a new column, mapping \n to the superclass column as though it were omitted from this class. \n This is used for mixins that add new columns to an inherited superclass. \n \n .. seealso: \n \n``` \n`orm_inheritance_column_conflicts` \n \n``` \n .. versionadded:: 2.0.0b4 \n \n:param default: Passed directly to the \n `_schema.Column.default` parameter if the \n `_orm.mapped_column.insert_default` parameter is not present. \n Additionally, when used with `orm_declarative_native_dataclasses`, \n indicates a default Python value that should be applied to the keyword \n constructor within the generated ``__init__()`` method. \n \n Note that in the case of dataclass generation when \n `_orm.mapped_column.insert_default` is not present, this means \n the `_orm.mapped_column.default` value is used in **two** \n places, both the ``__init__()`` method as well as the \n `_schema.Column.default` parameter. While this behavior may \n change in a future release, for the moment this tends to \"work out\"; a \n default of ``None`` will mean that the `_schema.Column` gets no \n default generator, whereas a default that refers to a non-``None`` Python \n or SQL expression value will be assigned up front on the object when \n ``__init__()`` is called, which is the same value that the Core \n `_sql.Insert` construct would use in any case, leading to the same \n end result. \n \n .. note:: When using Core level column defaults that are callables to \n    be interpreted by the underlying `_schema.Column` in conjunction \n    with `ORM-mapped dataclasses \n    `, especially those that are \n    `context-aware default functions `, \n    **the** `_orm.mapped_column.insert_default` **parameter must \n    be used instead**. This is necessary to disambiguate the callable from \n    being interpreted as a dataclass level default. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.insert_default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param insert_default: Passed directly to the \n `_schema.Column.default` parameter; will supersede the value \n of `_orm.mapped_column.default` when present, however \n `_orm.mapped_column.default` will always apply to the \n constructor default for a dataclasses mapping. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param sort_order: An integer that indicates how this mapped column \n should be sorted compared to the others when the ORM is creating a \n `_schema.Table`. Among mapped columns that have the same \n value the default ordering is used, placing first the mapped columns \n defined in the main class, then the ones in the super classes. \n Defaults to 0. The sort is ascending. \n \n .. versionadded:: 2.0.4 \n \n:param active_history=False: \n \n    When ``True``, indicates that the \"previous\" value for a \n    scalar attribute should be loaded when replaced, if not \n    already loaded. Normally, history tracking logic for \n    simple non-primary-key scalar values only needs to be \n    aware of the \"new\" value in order to perform a flush. This \n    flag is available for applications that make use of \n    `.attributes.get_history` or `.Session.is_modified` \n    which also need to know the \"previous\" value of the attribute. \n \n    .. versionadded:: 2.0.10 \n \n \n:param init: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__init__()`` \n method as generated by the dataclass process. \n:param repr: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__repr__()`` \n method as generated by the dataclass process. \n:param default_factory: Specific to \n `orm_declarative_native_dataclasses`, \n specifies a default-value generation function that will take place \n as part of the ``__init__()`` \n method as generated by the dataclass process. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.insert_default` \n \n``` \n:param compare: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be included in comparison operations when generating the \n ``__eq__()`` and ``__ne__()`` methods for the mapped class. \n \n .. versionadded:: 2.0.0b4 \n \n:param kw_only: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be marked as keyword-only when generating the ``__init__()``. \n \n:param hash: Specific to \n `orm_declarative_native_dataclasses`, controls if this field \n is included when generating the ``__hash__()`` method for the mapped \n class. \n \n .. versionadded:: 2.0.36 \n \n:param dataclass_metadata: Specific to \n `orm_declarative_native_dataclasses`, supplies metadata \n to be attached to the generated dataclass field. \n \n .. versionadded:: 2.0.42 \n \n:param \\**kw: All remaining keyword arguments are passed through to the \n constructor for the `_schema.Column`.\n\nGo to [SchemaEventTarget](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L1823,7) | [TypeEngine](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/type_api.py#L116,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [_NoArg](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L118,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [SchemaConst](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L145,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3627,7) | [ColumnElement](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L1287,7) | [FetchedValue](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L4706,7) | [TextClause](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L2545,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L862,7) | [MappedColumn](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/properties.py#L516,7)"}}} -{"suite": "sqlalchemy", "label": "sessionmaker hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 12, "character": 27, "iteration": 3, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(function) mapped_column: def mapped_column[_T](\n __name_pos : SchemaEventTarget | TypeEngine[Any] | str | type[TypeEngine[Any]] | None = None,\n __type_pos : SchemaEventTarget | TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n /,\n *args : SchemaEventTarget,\n *,\n init : _NoArg | bool = _NoArg.NO_ARG,\n repr : _NoArg | bool = _NoArg.NO_ARG,\n default : Any | None = _NoArg.NO_ARG,\n default_factory : (() -> _T) | _NoArg = _NoArg.NO_ARG,\n compare : _NoArg | bool = _NoArg.NO_ARG,\n kw_only : _NoArg | bool = _NoArg.NO_ARG,\n hash : _NoArg | bool | None = _NoArg.NO_ARG,\n nullable : Literal[SchemaConst.NULL_UNSPECIFIED] | bool | None = SchemaConst.NULL_UNSPECIFIED,\n primary_key : bool | None = False,\n deferred : _NoArg | bool = _NoArg.NO_ARG,\n deferred_group : str | None = None,\n deferred_raiseload : bool | None = None,\n use_existing_column: bool = False,\n name : str | None = None,\n type_ : TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n autoincrement : _AutoIncrementType = 'auto',\n doc : str | None = None,\n key : str | None = None,\n index : bool | None = None,\n unique : bool | None = None,\n info : dict[Any, Any] | None = None,\n onupdate : Any | None = None,\n insert_default : Any | None = _NoArg.NO_ARG,\n server_default : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n server_onupdate : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n active_history : bool = False,\n quote : bool | None = None,\n system : bool = False,\n comment : str | None = None,\n sort_order : _NoArg | int = _NoArg.NO_ARG,\n dataclass_metadata : Mapping[Any, Any] | _NoArg | None = _NoArg.NO_ARG,\n **kw : Any\n) -> MappedColumn[Any]: ...\n```\n---\ndeclare a new ORM-mapped `_schema.Column` construct \nfor use within `Declarative Table ` \nconfiguration. \n \nThe `_orm.mapped_column` function provides an ORM-aware and \nPython-typing-compatible construct which is used with \n`declarative ` mappings to indicate an \nattribute that's mapped to a Core `_schema.Column` object. It \nprovides the equivalent feature as mapping an attribute to a \n`_schema.Column` object directly when using Declarative, \nspecifically when using `Declarative Table ` \nconfiguration. \n \n.. versionadded:: 2.0 \n \n`_orm.mapped_column` is normally used with explicit typing along with \nthe `_orm.Mapped` annotation type, where it can derive the SQL \ntype and nullability for the column based on what's present within the \n`_orm.Mapped` annotation. It also may be used without annotations \nas a drop-in replacement for how `_schema.Column` is used in \nDeclarative mappings in SQLAlchemy 1.x style. \n \nFor usage examples of `_orm.mapped_column`, see the documentation \nat `orm_declarative_table`. \n \n.. seealso: \n \n``` \n`orm_declarative_table` - complete documentation \n \n`whatsnew_20_orm_declarative_typing` - migration notes for \nDeclarative mappings using 1.x style mappings \n \n``` \n:param __name: String name to give to the `_schema.Column`. This \n is an optional, positional only argument that if present must be the \n first positional argument passed. If omitted, the attribute name to \n which the `_orm.mapped_column` is mapped will be used as the SQL \n column name. \n:param __type: `_types.TypeEngine` type or instance which will \n indicate the datatype to be associated with the `_schema.Column`. \n This is an optional, positional-only argument that if present must \n immediately follow the ``__name`` parameter if present also, or otherwise \n be the first positional parameter. If omitted, the ultimate type for \n the column may be derived either from the annotated type, or if a \n `_schema.ForeignKey` is present, from the datatype of the \n referenced column. \n:param \\*args: Additional positional arguments include constructs such \n as `_schema.ForeignKey`, `_schema.CheckConstraint`, \n and `_schema.Identity`, which are passed through to the constructed \n `_schema.Column`. \n:param nullable: Optional bool, whether the column should be \"NULL\" or \n \"NOT NULL\". If omitted, the nullability is derived from the type \n annotation based on whether or not ``typing.Optional`` (or its equivalent) \n is present. ``nullable`` defaults to ``True`` otherwise for non-primary \n key columns, and ``False`` for primary key columns. \n:param primary_key: optional bool, indicates the `_schema.Column` \n would be part of the table's primary key or not. \n:param deferred: Optional bool - this keyword argument is consumed by the \n ORM declarative process, and is not part of the `_schema.Column` \n itself; instead, it indicates that this column should be \"deferred\" for \n loading as though mapped by `_orm.deferred`. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_declarative` \n \n``` \n:param deferred_group: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.group` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_group` \n \n``` \n:param deferred_raiseload: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.raiseload` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_raiseload` \n \n``` \n:param use_existing_column: if True, will attempt to locate the given \n column name on an inherited superclass (typically single inheriting \n superclass), and if present, will not produce a new column, mapping \n to the superclass column as though it were omitted from this class. \n This is used for mixins that add new columns to an inherited superclass. \n \n .. seealso: \n \n``` \n`orm_inheritance_column_conflicts` \n \n``` \n .. versionadded:: 2.0.0b4 \n \n:param default: Passed directly to the \n `_schema.Column.default` parameter if the \n `_orm.mapped_column.insert_default` parameter is not present. \n Additionally, when used with `orm_declarative_native_dataclasses`, \n indicates a default Python value that should be applied to the keyword \n constructor within the generated ``__init__()`` method. \n \n Note that in the case of dataclass generation when \n `_orm.mapped_column.insert_default` is not present, this means \n the `_orm.mapped_column.default` value is used in **two** \n places, both the ``__init__()`` method as well as the \n `_schema.Column.default` parameter. While this behavior may \n change in a future release, for the moment this tends to \"work out\"; a \n default of ``None`` will mean that the `_schema.Column` gets no \n default generator, whereas a default that refers to a non-``None`` Python \n or SQL expression value will be assigned up front on the object when \n ``__init__()`` is called, which is the same value that the Core \n `_sql.Insert` construct would use in any case, leading to the same \n end result. \n \n .. note:: When using Core level column defaults that are callables to \n    be interpreted by the underlying `_schema.Column` in conjunction \n    with `ORM-mapped dataclasses \n    `, especially those that are \n    `context-aware default functions `, \n    **the** `_orm.mapped_column.insert_default` **parameter must \n    be used instead**. This is necessary to disambiguate the callable from \n    being interpreted as a dataclass level default. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.insert_default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param insert_default: Passed directly to the \n `_schema.Column.default` parameter; will supersede the value \n of `_orm.mapped_column.default` when present, however \n `_orm.mapped_column.default` will always apply to the \n constructor default for a dataclasses mapping. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param sort_order: An integer that indicates how this mapped column \n should be sorted compared to the others when the ORM is creating a \n `_schema.Table`. Among mapped columns that have the same \n value the default ordering is used, placing first the mapped columns \n defined in the main class, then the ones in the super classes. \n Defaults to 0. The sort is ascending. \n \n .. versionadded:: 2.0.4 \n \n:param active_history=False: \n \n    When ``True``, indicates that the \"previous\" value for a \n    scalar attribute should be loaded when replaced, if not \n    already loaded. Normally, history tracking logic for \n    simple non-primary-key scalar values only needs to be \n    aware of the \"new\" value in order to perform a flush. This \n    flag is available for applications that make use of \n    `.attributes.get_history` or `.Session.is_modified` \n    which also need to know the \"previous\" value of the attribute. \n \n    .. versionadded:: 2.0.10 \n \n \n:param init: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__init__()`` \n method as generated by the dataclass process. \n:param repr: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__repr__()`` \n method as generated by the dataclass process. \n:param default_factory: Specific to \n `orm_declarative_native_dataclasses`, \n specifies a default-value generation function that will take place \n as part of the ``__init__()`` \n method as generated by the dataclass process. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.insert_default` \n \n``` \n:param compare: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be included in comparison operations when generating the \n ``__eq__()`` and ``__ne__()`` methods for the mapped class. \n \n .. versionadded:: 2.0.0b4 \n \n:param kw_only: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be marked as keyword-only when generating the ``__init__()``. \n \n:param hash: Specific to \n `orm_declarative_native_dataclasses`, controls if this field \n is included when generating the ``__hash__()`` method for the mapped \n class. \n \n .. versionadded:: 2.0.36 \n \n:param dataclass_metadata: Specific to \n `orm_declarative_native_dataclasses`, supplies metadata \n to be attached to the generated dataclass field. \n \n .. versionadded:: 2.0.42 \n \n:param \\**kw: All remaining keyword arguments are passed through to the \n constructor for the `_schema.Column`.\n\nGo to [SchemaEventTarget](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L1823,7) | [TypeEngine](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/type_api.py#L116,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [_NoArg](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L118,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [SchemaConst](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L145,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3627,7) | [ColumnElement](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L1287,7) | [FetchedValue](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L4706,7) | [TextClause](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L2545,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L862,7) | [MappedColumn](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/properties.py#L516,7)"}}} -{"suite": "sqlalchemy", "label": "sessionmaker hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 12, "character": 27, "iteration": 4, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(function) mapped_column: def mapped_column[_T](\n __name_pos : SchemaEventTarget | TypeEngine[Any] | str | type[TypeEngine[Any]] | None = None,\n __type_pos : SchemaEventTarget | TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n /,\n *args : SchemaEventTarget,\n *,\n init : _NoArg | bool = _NoArg.NO_ARG,\n repr : _NoArg | bool = _NoArg.NO_ARG,\n default : Any | None = _NoArg.NO_ARG,\n default_factory : (() -> _T) | _NoArg = _NoArg.NO_ARG,\n compare : _NoArg | bool = _NoArg.NO_ARG,\n kw_only : _NoArg | bool = _NoArg.NO_ARG,\n hash : _NoArg | bool | None = _NoArg.NO_ARG,\n nullable : Literal[SchemaConst.NULL_UNSPECIFIED] | bool | None = SchemaConst.NULL_UNSPECIFIED,\n primary_key : bool | None = False,\n deferred : _NoArg | bool = _NoArg.NO_ARG,\n deferred_group : str | None = None,\n deferred_raiseload : bool | None = None,\n use_existing_column: bool = False,\n name : str | None = None,\n type_ : TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n autoincrement : _AutoIncrementType = 'auto',\n doc : str | None = None,\n key : str | None = None,\n index : bool | None = None,\n unique : bool | None = None,\n info : dict[Any, Any] | None = None,\n onupdate : Any | None = None,\n insert_default : Any | None = _NoArg.NO_ARG,\n server_default : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n server_onupdate : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n active_history : bool = False,\n quote : bool | None = None,\n system : bool = False,\n comment : str | None = None,\n sort_order : _NoArg | int = _NoArg.NO_ARG,\n dataclass_metadata : Mapping[Any, Any] | _NoArg | None = _NoArg.NO_ARG,\n **kw : Any\n) -> MappedColumn[Any]: ...\n```\n---\ndeclare a new ORM-mapped `_schema.Column` construct \nfor use within `Declarative Table ` \nconfiguration. \n \nThe `_orm.mapped_column` function provides an ORM-aware and \nPython-typing-compatible construct which is used with \n`declarative ` mappings to indicate an \nattribute that's mapped to a Core `_schema.Column` object. It \nprovides the equivalent feature as mapping an attribute to a \n`_schema.Column` object directly when using Declarative, \nspecifically when using `Declarative Table ` \nconfiguration. \n \n.. versionadded:: 2.0 \n \n`_orm.mapped_column` is normally used with explicit typing along with \nthe `_orm.Mapped` annotation type, where it can derive the SQL \ntype and nullability for the column based on what's present within the \n`_orm.Mapped` annotation. It also may be used without annotations \nas a drop-in replacement for how `_schema.Column` is used in \nDeclarative mappings in SQLAlchemy 1.x style. \n \nFor usage examples of `_orm.mapped_column`, see the documentation \nat `orm_declarative_table`. \n \n.. seealso: \n \n``` \n`orm_declarative_table` - complete documentation \n \n`whatsnew_20_orm_declarative_typing` - migration notes for \nDeclarative mappings using 1.x style mappings \n \n``` \n:param __name: String name to give to the `_schema.Column`. This \n is an optional, positional only argument that if present must be the \n first positional argument passed. If omitted, the attribute name to \n which the `_orm.mapped_column` is mapped will be used as the SQL \n column name. \n:param __type: `_types.TypeEngine` type or instance which will \n indicate the datatype to be associated with the `_schema.Column`. \n This is an optional, positional-only argument that if present must \n immediately follow the ``__name`` parameter if present also, or otherwise \n be the first positional parameter. If omitted, the ultimate type for \n the column may be derived either from the annotated type, or if a \n `_schema.ForeignKey` is present, from the datatype of the \n referenced column. \n:param \\*args: Additional positional arguments include constructs such \n as `_schema.ForeignKey`, `_schema.CheckConstraint`, \n and `_schema.Identity`, which are passed through to the constructed \n `_schema.Column`. \n:param nullable: Optional bool, whether the column should be \"NULL\" or \n \"NOT NULL\". If omitted, the nullability is derived from the type \n annotation based on whether or not ``typing.Optional`` (or its equivalent) \n is present. ``nullable`` defaults to ``True`` otherwise for non-primary \n key columns, and ``False`` for primary key columns. \n:param primary_key: optional bool, indicates the `_schema.Column` \n would be part of the table's primary key or not. \n:param deferred: Optional bool - this keyword argument is consumed by the \n ORM declarative process, and is not part of the `_schema.Column` \n itself; instead, it indicates that this column should be \"deferred\" for \n loading as though mapped by `_orm.deferred`. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_declarative` \n \n``` \n:param deferred_group: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.group` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_group` \n \n``` \n:param deferred_raiseload: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.raiseload` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_raiseload` \n \n``` \n:param use_existing_column: if True, will attempt to locate the given \n column name on an inherited superclass (typically single inheriting \n superclass), and if present, will not produce a new column, mapping \n to the superclass column as though it were omitted from this class. \n This is used for mixins that add new columns to an inherited superclass. \n \n .. seealso: \n \n``` \n`orm_inheritance_column_conflicts` \n \n``` \n .. versionadded:: 2.0.0b4 \n \n:param default: Passed directly to the \n `_schema.Column.default` parameter if the \n `_orm.mapped_column.insert_default` parameter is not present. \n Additionally, when used with `orm_declarative_native_dataclasses`, \n indicates a default Python value that should be applied to the keyword \n constructor within the generated ``__init__()`` method. \n \n Note that in the case of dataclass generation when \n `_orm.mapped_column.insert_default` is not present, this means \n the `_orm.mapped_column.default` value is used in **two** \n places, both the ``__init__()`` method as well as the \n `_schema.Column.default` parameter. While this behavior may \n change in a future release, for the moment this tends to \"work out\"; a \n default of ``None`` will mean that the `_schema.Column` gets no \n default generator, whereas a default that refers to a non-``None`` Python \n or SQL expression value will be assigned up front on the object when \n ``__init__()`` is called, which is the same value that the Core \n `_sql.Insert` construct would use in any case, leading to the same \n end result. \n \n .. note:: When using Core level column defaults that are callables to \n    be interpreted by the underlying `_schema.Column` in conjunction \n    with `ORM-mapped dataclasses \n    `, especially those that are \n    `context-aware default functions `, \n    **the** `_orm.mapped_column.insert_default` **parameter must \n    be used instead**. This is necessary to disambiguate the callable from \n    being interpreted as a dataclass level default. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.insert_default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param insert_default: Passed directly to the \n `_schema.Column.default` parameter; will supersede the value \n of `_orm.mapped_column.default` when present, however \n `_orm.mapped_column.default` will always apply to the \n constructor default for a dataclasses mapping. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param sort_order: An integer that indicates how this mapped column \n should be sorted compared to the others when the ORM is creating a \n `_schema.Table`. Among mapped columns that have the same \n value the default ordering is used, placing first the mapped columns \n defined in the main class, then the ones in the super classes. \n Defaults to 0. The sort is ascending. \n \n .. versionadded:: 2.0.4 \n \n:param active_history=False: \n \n    When ``True``, indicates that the \"previous\" value for a \n    scalar attribute should be loaded when replaced, if not \n    already loaded. Normally, history tracking logic for \n    simple non-primary-key scalar values only needs to be \n    aware of the \"new\" value in order to perform a flush. This \n    flag is available for applications that make use of \n    `.attributes.get_history` or `.Session.is_modified` \n    which also need to know the \"previous\" value of the attribute. \n \n    .. versionadded:: 2.0.10 \n \n \n:param init: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__init__()`` \n method as generated by the dataclass process. \n:param repr: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__repr__()`` \n method as generated by the dataclass process. \n:param default_factory: Specific to \n `orm_declarative_native_dataclasses`, \n specifies a default-value generation function that will take place \n as part of the ``__init__()`` \n method as generated by the dataclass process. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.insert_default` \n \n``` \n:param compare: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be included in comparison operations when generating the \n ``__eq__()`` and ``__ne__()`` methods for the mapped class. \n \n .. versionadded:: 2.0.0b4 \n \n:param kw_only: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be marked as keyword-only when generating the ``__init__()``. \n \n:param hash: Specific to \n `orm_declarative_native_dataclasses`, controls if this field \n is included when generating the ``__hash__()`` method for the mapped \n class. \n \n .. versionadded:: 2.0.36 \n \n:param dataclass_metadata: Specific to \n `orm_declarative_native_dataclasses`, supplies metadata \n to be attached to the generated dataclass field. \n \n .. versionadded:: 2.0.42 \n \n:param \\**kw: All remaining keyword arguments are passed through to the \n constructor for the `_schema.Column`.\n\nGo to [SchemaEventTarget](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L1823,7) | [TypeEngine](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/type_api.py#L116,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [_NoArg](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L118,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [SchemaConst](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L145,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3627,7) | [ColumnElement](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L1287,7) | [FetchedValue](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L4706,7) | [TextClause](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L2545,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L862,7) | [MappedColumn](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/properties.py#L516,7)"}}} -{"suite": "sqlalchemy", "label": "sessionmaker hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 12, "character": 27, "iteration": 5, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(function) mapped_column: def mapped_column[_T](\n __name_pos : SchemaEventTarget | TypeEngine[Any] | str | type[TypeEngine[Any]] | None = None,\n __type_pos : SchemaEventTarget | TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n /,\n *args : SchemaEventTarget,\n *,\n init : _NoArg | bool = _NoArg.NO_ARG,\n repr : _NoArg | bool = _NoArg.NO_ARG,\n default : Any | None = _NoArg.NO_ARG,\n default_factory : (() -> _T) | _NoArg = _NoArg.NO_ARG,\n compare : _NoArg | bool = _NoArg.NO_ARG,\n kw_only : _NoArg | bool = _NoArg.NO_ARG,\n hash : _NoArg | bool | None = _NoArg.NO_ARG,\n nullable : Literal[SchemaConst.NULL_UNSPECIFIED] | bool | None = SchemaConst.NULL_UNSPECIFIED,\n primary_key : bool | None = False,\n deferred : _NoArg | bool = _NoArg.NO_ARG,\n deferred_group : str | None = None,\n deferred_raiseload : bool | None = None,\n use_existing_column: bool = False,\n name : str | None = None,\n type_ : TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n autoincrement : _AutoIncrementType = 'auto',\n doc : str | None = None,\n key : str | None = None,\n index : bool | None = None,\n unique : bool | None = None,\n info : dict[Any, Any] | None = None,\n onupdate : Any | None = None,\n insert_default : Any | None = _NoArg.NO_ARG,\n server_default : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n server_onupdate : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n active_history : bool = False,\n quote : bool | None = None,\n system : bool = False,\n comment : str | None = None,\n sort_order : _NoArg | int = _NoArg.NO_ARG,\n dataclass_metadata : Mapping[Any, Any] | _NoArg | None = _NoArg.NO_ARG,\n **kw : Any\n) -> MappedColumn[Any]: ...\n```\n---\ndeclare a new ORM-mapped `_schema.Column` construct \nfor use within `Declarative Table ` \nconfiguration. \n \nThe `_orm.mapped_column` function provides an ORM-aware and \nPython-typing-compatible construct which is used with \n`declarative ` mappings to indicate an \nattribute that's mapped to a Core `_schema.Column` object. It \nprovides the equivalent feature as mapping an attribute to a \n`_schema.Column` object directly when using Declarative, \nspecifically when using `Declarative Table ` \nconfiguration. \n \n.. versionadded:: 2.0 \n \n`_orm.mapped_column` is normally used with explicit typing along with \nthe `_orm.Mapped` annotation type, where it can derive the SQL \ntype and nullability for the column based on what's present within the \n`_orm.Mapped` annotation. It also may be used without annotations \nas a drop-in replacement for how `_schema.Column` is used in \nDeclarative mappings in SQLAlchemy 1.x style. \n \nFor usage examples of `_orm.mapped_column`, see the documentation \nat `orm_declarative_table`. \n \n.. seealso: \n \n``` \n`orm_declarative_table` - complete documentation \n \n`whatsnew_20_orm_declarative_typing` - migration notes for \nDeclarative mappings using 1.x style mappings \n \n``` \n:param __name: String name to give to the `_schema.Column`. This \n is an optional, positional only argument that if present must be the \n first positional argument passed. If omitted, the attribute name to \n which the `_orm.mapped_column` is mapped will be used as the SQL \n column name. \n:param __type: `_types.TypeEngine` type or instance which will \n indicate the datatype to be associated with the `_schema.Column`. \n This is an optional, positional-only argument that if present must \n immediately follow the ``__name`` parameter if present also, or otherwise \n be the first positional parameter. If omitted, the ultimate type for \n the column may be derived either from the annotated type, or if a \n `_schema.ForeignKey` is present, from the datatype of the \n referenced column. \n:param \\*args: Additional positional arguments include constructs such \n as `_schema.ForeignKey`, `_schema.CheckConstraint`, \n and `_schema.Identity`, which are passed through to the constructed \n `_schema.Column`. \n:param nullable: Optional bool, whether the column should be \"NULL\" or \n \"NOT NULL\". If omitted, the nullability is derived from the type \n annotation based on whether or not ``typing.Optional`` (or its equivalent) \n is present. ``nullable`` defaults to ``True`` otherwise for non-primary \n key columns, and ``False`` for primary key columns. \n:param primary_key: optional bool, indicates the `_schema.Column` \n would be part of the table's primary key or not. \n:param deferred: Optional bool - this keyword argument is consumed by the \n ORM declarative process, and is not part of the `_schema.Column` \n itself; instead, it indicates that this column should be \"deferred\" for \n loading as though mapped by `_orm.deferred`. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_declarative` \n \n``` \n:param deferred_group: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.group` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_group` \n \n``` \n:param deferred_raiseload: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.raiseload` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_raiseload` \n \n``` \n:param use_existing_column: if True, will attempt to locate the given \n column name on an inherited superclass (typically single inheriting \n superclass), and if present, will not produce a new column, mapping \n to the superclass column as though it were omitted from this class. \n This is used for mixins that add new columns to an inherited superclass. \n \n .. seealso: \n \n``` \n`orm_inheritance_column_conflicts` \n \n``` \n .. versionadded:: 2.0.0b4 \n \n:param default: Passed directly to the \n `_schema.Column.default` parameter if the \n `_orm.mapped_column.insert_default` parameter is not present. \n Additionally, when used with `orm_declarative_native_dataclasses`, \n indicates a default Python value that should be applied to the keyword \n constructor within the generated ``__init__()`` method. \n \n Note that in the case of dataclass generation when \n `_orm.mapped_column.insert_default` is not present, this means \n the `_orm.mapped_column.default` value is used in **two** \n places, both the ``__init__()`` method as well as the \n `_schema.Column.default` parameter. While this behavior may \n change in a future release, for the moment this tends to \"work out\"; a \n default of ``None`` will mean that the `_schema.Column` gets no \n default generator, whereas a default that refers to a non-``None`` Python \n or SQL expression value will be assigned up front on the object when \n ``__init__()`` is called, which is the same value that the Core \n `_sql.Insert` construct would use in any case, leading to the same \n end result. \n \n .. note:: When using Core level column defaults that are callables to \n    be interpreted by the underlying `_schema.Column` in conjunction \n    with `ORM-mapped dataclasses \n    `, especially those that are \n    `context-aware default functions `, \n    **the** `_orm.mapped_column.insert_default` **parameter must \n    be used instead**. This is necessary to disambiguate the callable from \n    being interpreted as a dataclass level default. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.insert_default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param insert_default: Passed directly to the \n `_schema.Column.default` parameter; will supersede the value \n of `_orm.mapped_column.default` when present, however \n `_orm.mapped_column.default` will always apply to the \n constructor default for a dataclasses mapping. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param sort_order: An integer that indicates how this mapped column \n should be sorted compared to the others when the ORM is creating a \n `_schema.Table`. Among mapped columns that have the same \n value the default ordering is used, placing first the mapped columns \n defined in the main class, then the ones in the super classes. \n Defaults to 0. The sort is ascending. \n \n .. versionadded:: 2.0.4 \n \n:param active_history=False: \n \n    When ``True``, indicates that the \"previous\" value for a \n    scalar attribute should be loaded when replaced, if not \n    already loaded. Normally, history tracking logic for \n    simple non-primary-key scalar values only needs to be \n    aware of the \"new\" value in order to perform a flush. This \n    flag is available for applications that make use of \n    `.attributes.get_history` or `.Session.is_modified` \n    which also need to know the \"previous\" value of the attribute. \n \n    .. versionadded:: 2.0.10 \n \n \n:param init: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__init__()`` \n method as generated by the dataclass process. \n:param repr: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__repr__()`` \n method as generated by the dataclass process. \n:param default_factory: Specific to \n `orm_declarative_native_dataclasses`, \n specifies a default-value generation function that will take place \n as part of the ``__init__()`` \n method as generated by the dataclass process. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.insert_default` \n \n``` \n:param compare: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be included in comparison operations when generating the \n ``__eq__()`` and ``__ne__()`` methods for the mapped class. \n \n .. versionadded:: 2.0.0b4 \n \n:param kw_only: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be marked as keyword-only when generating the ``__init__()``. \n \n:param hash: Specific to \n `orm_declarative_native_dataclasses`, controls if this field \n is included when generating the ``__hash__()`` method for the mapped \n class. \n \n .. versionadded:: 2.0.36 \n \n:param dataclass_metadata: Specific to \n `orm_declarative_native_dataclasses`, supplies metadata \n to be attached to the generated dataclass field. \n \n .. versionadded:: 2.0.42 \n \n:param \\**kw: All remaining keyword arguments are passed through to the \n constructor for the `_schema.Column`.\n\nGo to [SchemaEventTarget](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L1823,7) | [TypeEngine](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/type_api.py#L116,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [_NoArg](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L118,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [SchemaConst](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L145,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3627,7) | [ColumnElement](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L1287,7) | [FetchedValue](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L4706,7) | [TextClause](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L2545,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L862,7) | [MappedColumn](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/properties.py#L516,7)"}}} +{"suite": "sqlalchemy", "label": "sessionmaker hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 12, "character": 27, "iteration": 1, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(function) mapped_column: def mapped_column[_T](\n __name_pos : SchemaEventTarget | TypeEngine[Any] | str | type[TypeEngine[Any]] | None = None,\n __type_pos : SchemaEventTarget | TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n /,\n *args : SchemaEventTarget,\n *,\n init : _NoArg | bool = _NoArg.NO_ARG,\n repr : _NoArg | bool = _NoArg.NO_ARG,\n default : Any | None = _NoArg.NO_ARG,\n default_factory : (() -> _T) | _NoArg = _NoArg.NO_ARG,\n compare : _NoArg | bool = _NoArg.NO_ARG,\n kw_only : _NoArg | bool = _NoArg.NO_ARG,\n hash : _NoArg | bool | None = _NoArg.NO_ARG,\n nullable : Literal[SchemaConst.NULL_UNSPECIFIED] | bool | None = SchemaConst.NULL_UNSPECIFIED,\n primary_key : bool | None = False,\n deferred : _NoArg | bool = _NoArg.NO_ARG,\n deferred_group : str | None = None,\n deferred_raiseload : bool | None = None,\n use_existing_column: bool = False,\n name : str | None = None,\n type_ : TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n autoincrement : _AutoIncrementType = 'auto',\n doc : str | None = None,\n key : str | None = None,\n index : bool | None = None,\n unique : bool | None = None,\n info : dict[Any, Any] | None = None,\n onupdate : Any | None = None,\n insert_default : Any | None = _NoArg.NO_ARG,\n server_default : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n server_onupdate : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n active_history : bool = False,\n quote : bool | None = None,\n system : bool = False,\n comment : str | None = None,\n sort_order : _NoArg | int = _NoArg.NO_ARG,\n dataclass_metadata : Mapping[Any, Any] | _NoArg | None = _NoArg.NO_ARG,\n **kw : Any\n) -> MappedColumn[Any]: ...\n```\n---\ndeclare a new ORM-mapped `_schema.Column` construct \nfor use within `Declarative Table ` \nconfiguration. \n \nThe `_orm.mapped_column` function provides an ORM-aware and \nPython-typing-compatible construct which is used with \n`declarative ` mappings to indicate an \nattribute that's mapped to a Core `_schema.Column` object. It \nprovides the equivalent feature as mapping an attribute to a \n`_schema.Column` object directly when using Declarative, \nspecifically when using `Declarative Table ` \nconfiguration. \n \n.. versionadded:: 2.0 \n \n`_orm.mapped_column` is normally used with explicit typing along with \nthe `_orm.Mapped` annotation type, where it can derive the SQL \ntype and nullability for the column based on what's present within the \n`_orm.Mapped` annotation. It also may be used without annotations \nas a drop-in replacement for how `_schema.Column` is used in \nDeclarative mappings in SQLAlchemy 1.x style. \n \nFor usage examples of `_orm.mapped_column`, see the documentation \nat `orm_declarative_table`. \n \n.. seealso: \n \n``` \n`orm_declarative_table` - complete documentation \n \n`whatsnew_20_orm_declarative_typing` - migration notes for \nDeclarative mappings using 1.x style mappings \n \n``` \n:param __name: String name to give to the `_schema.Column`. This \n is an optional, positional only argument that if present must be the \n first positional argument passed. If omitted, the attribute name to \n which the `_orm.mapped_column` is mapped will be used as the SQL \n column name. \n:param __type: `_types.TypeEngine` type or instance which will \n indicate the datatype to be associated with the `_schema.Column`. \n This is an optional, positional-only argument that if present must \n immediately follow the ``__name`` parameter if present also, or otherwise \n be the first positional parameter. If omitted, the ultimate type for \n the column may be derived either from the annotated type, or if a \n `_schema.ForeignKey` is present, from the datatype of the \n referenced column. \n:param \\*args: Additional positional arguments include constructs such \n as `_schema.ForeignKey`, `_schema.CheckConstraint`, \n and `_schema.Identity`, which are passed through to the constructed \n `_schema.Column`. \n:param nullable: Optional bool, whether the column should be \"NULL\" or \n \"NOT NULL\". If omitted, the nullability is derived from the type \n annotation based on whether or not ``typing.Optional`` (or its equivalent) \n is present. ``nullable`` defaults to ``True`` otherwise for non-primary \n key columns, and ``False`` for primary key columns. \n:param primary_key: optional bool, indicates the `_schema.Column` \n would be part of the table's primary key or not. \n:param deferred: Optional bool - this keyword argument is consumed by the \n ORM declarative process, and is not part of the `_schema.Column` \n itself; instead, it indicates that this column should be \"deferred\" for \n loading as though mapped by `_orm.deferred`. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_declarative` \n \n``` \n:param deferred_group: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.group` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_group` \n \n``` \n:param deferred_raiseload: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.raiseload` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_raiseload` \n \n``` \n:param use_existing_column: if True, will attempt to locate the given \n column name on an inherited superclass (typically single inheriting \n superclass), and if present, will not produce a new column, mapping \n to the superclass column as though it were omitted from this class. \n This is used for mixins that add new columns to an inherited superclass. \n \n .. seealso: \n \n``` \n`orm_inheritance_column_conflicts` \n \n``` \n .. versionadded:: 2.0.0b4 \n \n:param default: Passed directly to the \n `_schema.Column.default` parameter if the \n `_orm.mapped_column.insert_default` parameter is not present. \n Additionally, when used with `orm_declarative_native_dataclasses`, \n indicates a default Python value that should be applied to the keyword \n constructor within the generated ``__init__()`` method. \n \n Note that in the case of dataclass generation when \n `_orm.mapped_column.insert_default` is not present, this means \n the `_orm.mapped_column.default` value is used in **two** \n places, both the ``__init__()`` method as well as the \n `_schema.Column.default` parameter. While this behavior may \n change in a future release, for the moment this tends to \"work out\"; a \n default of ``None`` will mean that the `_schema.Column` gets no \n default generator, whereas a default that refers to a non-``None`` Python \n or SQL expression value will be assigned up front on the object when \n ``__init__()`` is called, which is the same value that the Core \n `_sql.Insert` construct would use in any case, leading to the same \n end result. \n \n .. note:: When using Core level column defaults that are callables to \n    be interpreted by the underlying `_schema.Column` in conjunction \n    with `ORM-mapped dataclasses \n    `, especially those that are \n    `context-aware default functions `, \n    **the** `_orm.mapped_column.insert_default` **parameter must \n    be used instead**. This is necessary to disambiguate the callable from \n    being interpreted as a dataclass level default. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.insert_default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param insert_default: Passed directly to the \n `_schema.Column.default` parameter; will supersede the value \n of `_orm.mapped_column.default` when present, however \n `_orm.mapped_column.default` will always apply to the \n constructor default for a dataclasses mapping. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param sort_order: An integer that indicates how this mapped column \n should be sorted compared to the others when the ORM is creating a \n `_schema.Table`. Among mapped columns that have the same \n value the default ordering is used, placing first the mapped columns \n defined in the main class, then the ones in the super classes. \n Defaults to 0. The sort is ascending. \n \n .. versionadded:: 2.0.4 \n \n:param active_history=False: \n \n    When ``True``, indicates that the \"previous\" value for a \n    scalar attribute should be loaded when replaced, if not \n    already loaded. Normally, history tracking logic for \n    simple non-primary-key scalar values only needs to be \n    aware of the \"new\" value in order to perform a flush. This \n    flag is available for applications that make use of \n    `.attributes.get_history` or `.Session.is_modified` \n    which also need to know the \"previous\" value of the attribute. \n \n    .. versionadded:: 2.0.10 \n \n \n:param init: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__init__()`` \n method as generated by the dataclass process. \n:param repr: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__repr__()`` \n method as generated by the dataclass process. \n:param default_factory: Specific to \n `orm_declarative_native_dataclasses`, \n specifies a default-value generation function that will take place \n as part of the ``__init__()`` \n method as generated by the dataclass process. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.insert_default` \n \n``` \n:param compare: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be included in comparison operations when generating the \n ``__eq__()`` and ``__ne__()`` methods for the mapped class. \n \n .. versionadded:: 2.0.0b4 \n \n:param kw_only: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be marked as keyword-only when generating the ``__init__()``. \n \n:param hash: Specific to \n `orm_declarative_native_dataclasses`, controls if this field \n is included when generating the ``__hash__()`` method for the mapped \n class. \n \n .. versionadded:: 2.0.36 \n \n:param dataclass_metadata: Specific to \n `orm_declarative_native_dataclasses`, supplies metadata \n to be attached to the generated dataclass field. \n \n .. versionadded:: 2.0.42 \n \n:param \\**kw: All remaining keyword arguments are passed through to the \n constructor for the `_schema.Column`.\n\nGo to [SchemaEventTarget](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L1823,7) | [TypeEngine](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/type_api.py#L116,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [_NoArg](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L118,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [SchemaConst](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L145,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3627,7) | [ColumnElement](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L1287,7) | [FetchedValue](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L4706,7) | [TextClause](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L2545,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L862,7) | [MappedColumn](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/properties.py#L516,7)"}}} +{"suite": "sqlalchemy", "label": "sessionmaker hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 12, "character": 27, "iteration": 2, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(function) mapped_column: def mapped_column[_T](\n __name_pos : SchemaEventTarget | TypeEngine[Any] | str | type[TypeEngine[Any]] | None = None,\n __type_pos : SchemaEventTarget | TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n /,\n *args : SchemaEventTarget,\n *,\n init : _NoArg | bool = _NoArg.NO_ARG,\n repr : _NoArg | bool = _NoArg.NO_ARG,\n default : Any | None = _NoArg.NO_ARG,\n default_factory : (() -> _T) | _NoArg = _NoArg.NO_ARG,\n compare : _NoArg | bool = _NoArg.NO_ARG,\n kw_only : _NoArg | bool = _NoArg.NO_ARG,\n hash : _NoArg | bool | None = _NoArg.NO_ARG,\n nullable : Literal[SchemaConst.NULL_UNSPECIFIED] | bool | None = SchemaConst.NULL_UNSPECIFIED,\n primary_key : bool | None = False,\n deferred : _NoArg | bool = _NoArg.NO_ARG,\n deferred_group : str | None = None,\n deferred_raiseload : bool | None = None,\n use_existing_column: bool = False,\n name : str | None = None,\n type_ : TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n autoincrement : _AutoIncrementType = 'auto',\n doc : str | None = None,\n key : str | None = None,\n index : bool | None = None,\n unique : bool | None = None,\n info : dict[Any, Any] | None = None,\n onupdate : Any | None = None,\n insert_default : Any | None = _NoArg.NO_ARG,\n server_default : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n server_onupdate : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n active_history : bool = False,\n quote : bool | None = None,\n system : bool = False,\n comment : str | None = None,\n sort_order : _NoArg | int = _NoArg.NO_ARG,\n dataclass_metadata : Mapping[Any, Any] | _NoArg | None = _NoArg.NO_ARG,\n **kw : Any\n) -> MappedColumn[Any]: ...\n```\n---\ndeclare a new ORM-mapped `_schema.Column` construct \nfor use within `Declarative Table ` \nconfiguration. \n \nThe `_orm.mapped_column` function provides an ORM-aware and \nPython-typing-compatible construct which is used with \n`declarative ` mappings to indicate an \nattribute that's mapped to a Core `_schema.Column` object. It \nprovides the equivalent feature as mapping an attribute to a \n`_schema.Column` object directly when using Declarative, \nspecifically when using `Declarative Table ` \nconfiguration. \n \n.. versionadded:: 2.0 \n \n`_orm.mapped_column` is normally used with explicit typing along with \nthe `_orm.Mapped` annotation type, where it can derive the SQL \ntype and nullability for the column based on what's present within the \n`_orm.Mapped` annotation. It also may be used without annotations \nas a drop-in replacement for how `_schema.Column` is used in \nDeclarative mappings in SQLAlchemy 1.x style. \n \nFor usage examples of `_orm.mapped_column`, see the documentation \nat `orm_declarative_table`. \n \n.. seealso: \n \n``` \n`orm_declarative_table` - complete documentation \n \n`whatsnew_20_orm_declarative_typing` - migration notes for \nDeclarative mappings using 1.x style mappings \n \n``` \n:param __name: String name to give to the `_schema.Column`. This \n is an optional, positional only argument that if present must be the \n first positional argument passed. If omitted, the attribute name to \n which the `_orm.mapped_column` is mapped will be used as the SQL \n column name. \n:param __type: `_types.TypeEngine` type or instance which will \n indicate the datatype to be associated with the `_schema.Column`. \n This is an optional, positional-only argument that if present must \n immediately follow the ``__name`` parameter if present also, or otherwise \n be the first positional parameter. If omitted, the ultimate type for \n the column may be derived either from the annotated type, or if a \n `_schema.ForeignKey` is present, from the datatype of the \n referenced column. \n:param \\*args: Additional positional arguments include constructs such \n as `_schema.ForeignKey`, `_schema.CheckConstraint`, \n and `_schema.Identity`, which are passed through to the constructed \n `_schema.Column`. \n:param nullable: Optional bool, whether the column should be \"NULL\" or \n \"NOT NULL\". If omitted, the nullability is derived from the type \n annotation based on whether or not ``typing.Optional`` (or its equivalent) \n is present. ``nullable`` defaults to ``True`` otherwise for non-primary \n key columns, and ``False`` for primary key columns. \n:param primary_key: optional bool, indicates the `_schema.Column` \n would be part of the table's primary key or not. \n:param deferred: Optional bool - this keyword argument is consumed by the \n ORM declarative process, and is not part of the `_schema.Column` \n itself; instead, it indicates that this column should be \"deferred\" for \n loading as though mapped by `_orm.deferred`. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_declarative` \n \n``` \n:param deferred_group: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.group` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_group` \n \n``` \n:param deferred_raiseload: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.raiseload` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_raiseload` \n \n``` \n:param use_existing_column: if True, will attempt to locate the given \n column name on an inherited superclass (typically single inheriting \n superclass), and if present, will not produce a new column, mapping \n to the superclass column as though it were omitted from this class. \n This is used for mixins that add new columns to an inherited superclass. \n \n .. seealso: \n \n``` \n`orm_inheritance_column_conflicts` \n \n``` \n .. versionadded:: 2.0.0b4 \n \n:param default: Passed directly to the \n `_schema.Column.default` parameter if the \n `_orm.mapped_column.insert_default` parameter is not present. \n Additionally, when used with `orm_declarative_native_dataclasses`, \n indicates a default Python value that should be applied to the keyword \n constructor within the generated ``__init__()`` method. \n \n Note that in the case of dataclass generation when \n `_orm.mapped_column.insert_default` is not present, this means \n the `_orm.mapped_column.default` value is used in **two** \n places, both the ``__init__()`` method as well as the \n `_schema.Column.default` parameter. While this behavior may \n change in a future release, for the moment this tends to \"work out\"; a \n default of ``None`` will mean that the `_schema.Column` gets no \n default generator, whereas a default that refers to a non-``None`` Python \n or SQL expression value will be assigned up front on the object when \n ``__init__()`` is called, which is the same value that the Core \n `_sql.Insert` construct would use in any case, leading to the same \n end result. \n \n .. note:: When using Core level column defaults that are callables to \n    be interpreted by the underlying `_schema.Column` in conjunction \n    with `ORM-mapped dataclasses \n    `, especially those that are \n    `context-aware default functions `, \n    **the** `_orm.mapped_column.insert_default` **parameter must \n    be used instead**. This is necessary to disambiguate the callable from \n    being interpreted as a dataclass level default. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.insert_default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param insert_default: Passed directly to the \n `_schema.Column.default` parameter; will supersede the value \n of `_orm.mapped_column.default` when present, however \n `_orm.mapped_column.default` will always apply to the \n constructor default for a dataclasses mapping. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param sort_order: An integer that indicates how this mapped column \n should be sorted compared to the others when the ORM is creating a \n `_schema.Table`. Among mapped columns that have the same \n value the default ordering is used, placing first the mapped columns \n defined in the main class, then the ones in the super classes. \n Defaults to 0. The sort is ascending. \n \n .. versionadded:: 2.0.4 \n \n:param active_history=False: \n \n    When ``True``, indicates that the \"previous\" value for a \n    scalar attribute should be loaded when replaced, if not \n    already loaded. Normally, history tracking logic for \n    simple non-primary-key scalar values only needs to be \n    aware of the \"new\" value in order to perform a flush. This \n    flag is available for applications that make use of \n    `.attributes.get_history` or `.Session.is_modified` \n    which also need to know the \"previous\" value of the attribute. \n \n    .. versionadded:: 2.0.10 \n \n \n:param init: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__init__()`` \n method as generated by the dataclass process. \n:param repr: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__repr__()`` \n method as generated by the dataclass process. \n:param default_factory: Specific to \n `orm_declarative_native_dataclasses`, \n specifies a default-value generation function that will take place \n as part of the ``__init__()`` \n method as generated by the dataclass process. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.insert_default` \n \n``` \n:param compare: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be included in comparison operations when generating the \n ``__eq__()`` and ``__ne__()`` methods for the mapped class. \n \n .. versionadded:: 2.0.0b4 \n \n:param kw_only: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be marked as keyword-only when generating the ``__init__()``. \n \n:param hash: Specific to \n `orm_declarative_native_dataclasses`, controls if this field \n is included when generating the ``__hash__()`` method for the mapped \n class. \n \n .. versionadded:: 2.0.36 \n \n:param dataclass_metadata: Specific to \n `orm_declarative_native_dataclasses`, supplies metadata \n to be attached to the generated dataclass field. \n \n .. versionadded:: 2.0.42 \n \n:param \\**kw: All remaining keyword arguments are passed through to the \n constructor for the `_schema.Column`.\n\nGo to [SchemaEventTarget](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L1823,7) | [TypeEngine](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/type_api.py#L116,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [_NoArg](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L118,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [SchemaConst](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L145,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3627,7) | [ColumnElement](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L1287,7) | [FetchedValue](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L4706,7) | [TextClause](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L2545,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L862,7) | [MappedColumn](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/properties.py#L516,7)"}}} +{"suite": "sqlalchemy", "label": "sessionmaker hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 12, "character": 27, "iteration": 3, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(function) mapped_column: def mapped_column[_T](\n __name_pos : SchemaEventTarget | TypeEngine[Any] | str | type[TypeEngine[Any]] | None = None,\n __type_pos : SchemaEventTarget | TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n /,\n *args : SchemaEventTarget,\n *,\n init : _NoArg | bool = _NoArg.NO_ARG,\n repr : _NoArg | bool = _NoArg.NO_ARG,\n default : Any | None = _NoArg.NO_ARG,\n default_factory : (() -> _T) | _NoArg = _NoArg.NO_ARG,\n compare : _NoArg | bool = _NoArg.NO_ARG,\n kw_only : _NoArg | bool = _NoArg.NO_ARG,\n hash : _NoArg | bool | None = _NoArg.NO_ARG,\n nullable : Literal[SchemaConst.NULL_UNSPECIFIED] | bool | None = SchemaConst.NULL_UNSPECIFIED,\n primary_key : bool | None = False,\n deferred : _NoArg | bool = _NoArg.NO_ARG,\n deferred_group : str | None = None,\n deferred_raiseload : bool | None = None,\n use_existing_column: bool = False,\n name : str | None = None,\n type_ : TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n autoincrement : _AutoIncrementType = 'auto',\n doc : str | None = None,\n key : str | None = None,\n index : bool | None = None,\n unique : bool | None = None,\n info : dict[Any, Any] | None = None,\n onupdate : Any | None = None,\n insert_default : Any | None = _NoArg.NO_ARG,\n server_default : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n server_onupdate : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n active_history : bool = False,\n quote : bool | None = None,\n system : bool = False,\n comment : str | None = None,\n sort_order : _NoArg | int = _NoArg.NO_ARG,\n dataclass_metadata : Mapping[Any, Any] | _NoArg | None = _NoArg.NO_ARG,\n **kw : Any\n) -> MappedColumn[Any]: ...\n```\n---\ndeclare a new ORM-mapped `_schema.Column` construct \nfor use within `Declarative Table ` \nconfiguration. \n \nThe `_orm.mapped_column` function provides an ORM-aware and \nPython-typing-compatible construct which is used with \n`declarative ` mappings to indicate an \nattribute that's mapped to a Core `_schema.Column` object. It \nprovides the equivalent feature as mapping an attribute to a \n`_schema.Column` object directly when using Declarative, \nspecifically when using `Declarative Table ` \nconfiguration. \n \n.. versionadded:: 2.0 \n \n`_orm.mapped_column` is normally used with explicit typing along with \nthe `_orm.Mapped` annotation type, where it can derive the SQL \ntype and nullability for the column based on what's present within the \n`_orm.Mapped` annotation. It also may be used without annotations \nas a drop-in replacement for how `_schema.Column` is used in \nDeclarative mappings in SQLAlchemy 1.x style. \n \nFor usage examples of `_orm.mapped_column`, see the documentation \nat `orm_declarative_table`. \n \n.. seealso: \n \n``` \n`orm_declarative_table` - complete documentation \n \n`whatsnew_20_orm_declarative_typing` - migration notes for \nDeclarative mappings using 1.x style mappings \n \n``` \n:param __name: String name to give to the `_schema.Column`. This \n is an optional, positional only argument that if present must be the \n first positional argument passed. If omitted, the attribute name to \n which the `_orm.mapped_column` is mapped will be used as the SQL \n column name. \n:param __type: `_types.TypeEngine` type or instance which will \n indicate the datatype to be associated with the `_schema.Column`. \n This is an optional, positional-only argument that if present must \n immediately follow the ``__name`` parameter if present also, or otherwise \n be the first positional parameter. If omitted, the ultimate type for \n the column may be derived either from the annotated type, or if a \n `_schema.ForeignKey` is present, from the datatype of the \n referenced column. \n:param \\*args: Additional positional arguments include constructs such \n as `_schema.ForeignKey`, `_schema.CheckConstraint`, \n and `_schema.Identity`, which are passed through to the constructed \n `_schema.Column`. \n:param nullable: Optional bool, whether the column should be \"NULL\" or \n \"NOT NULL\". If omitted, the nullability is derived from the type \n annotation based on whether or not ``typing.Optional`` (or its equivalent) \n is present. ``nullable`` defaults to ``True`` otherwise for non-primary \n key columns, and ``False`` for primary key columns. \n:param primary_key: optional bool, indicates the `_schema.Column` \n would be part of the table's primary key or not. \n:param deferred: Optional bool - this keyword argument is consumed by the \n ORM declarative process, and is not part of the `_schema.Column` \n itself; instead, it indicates that this column should be \"deferred\" for \n loading as though mapped by `_orm.deferred`. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_declarative` \n \n``` \n:param deferred_group: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.group` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_group` \n \n``` \n:param deferred_raiseload: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.raiseload` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_raiseload` \n \n``` \n:param use_existing_column: if True, will attempt to locate the given \n column name on an inherited superclass (typically single inheriting \n superclass), and if present, will not produce a new column, mapping \n to the superclass column as though it were omitted from this class. \n This is used for mixins that add new columns to an inherited superclass. \n \n .. seealso: \n \n``` \n`orm_inheritance_column_conflicts` \n \n``` \n .. versionadded:: 2.0.0b4 \n \n:param default: Passed directly to the \n `_schema.Column.default` parameter if the \n `_orm.mapped_column.insert_default` parameter is not present. \n Additionally, when used with `orm_declarative_native_dataclasses`, \n indicates a default Python value that should be applied to the keyword \n constructor within the generated ``__init__()`` method. \n \n Note that in the case of dataclass generation when \n `_orm.mapped_column.insert_default` is not present, this means \n the `_orm.mapped_column.default` value is used in **two** \n places, both the ``__init__()`` method as well as the \n `_schema.Column.default` parameter. While this behavior may \n change in a future release, for the moment this tends to \"work out\"; a \n default of ``None`` will mean that the `_schema.Column` gets no \n default generator, whereas a default that refers to a non-``None`` Python \n or SQL expression value will be assigned up front on the object when \n ``__init__()`` is called, which is the same value that the Core \n `_sql.Insert` construct would use in any case, leading to the same \n end result. \n \n .. note:: When using Core level column defaults that are callables to \n    be interpreted by the underlying `_schema.Column` in conjunction \n    with `ORM-mapped dataclasses \n    `, especially those that are \n    `context-aware default functions `, \n    **the** `_orm.mapped_column.insert_default` **parameter must \n    be used instead**. This is necessary to disambiguate the callable from \n    being interpreted as a dataclass level default. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.insert_default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param insert_default: Passed directly to the \n `_schema.Column.default` parameter; will supersede the value \n of `_orm.mapped_column.default` when present, however \n `_orm.mapped_column.default` will always apply to the \n constructor default for a dataclasses mapping. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param sort_order: An integer that indicates how this mapped column \n should be sorted compared to the others when the ORM is creating a \n `_schema.Table`. Among mapped columns that have the same \n value the default ordering is used, placing first the mapped columns \n defined in the main class, then the ones in the super classes. \n Defaults to 0. The sort is ascending. \n \n .. versionadded:: 2.0.4 \n \n:param active_history=False: \n \n    When ``True``, indicates that the \"previous\" value for a \n    scalar attribute should be loaded when replaced, if not \n    already loaded. Normally, history tracking logic for \n    simple non-primary-key scalar values only needs to be \n    aware of the \"new\" value in order to perform a flush. This \n    flag is available for applications that make use of \n    `.attributes.get_history` or `.Session.is_modified` \n    which also need to know the \"previous\" value of the attribute. \n \n    .. versionadded:: 2.0.10 \n \n \n:param init: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__init__()`` \n method as generated by the dataclass process. \n:param repr: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__repr__()`` \n method as generated by the dataclass process. \n:param default_factory: Specific to \n `orm_declarative_native_dataclasses`, \n specifies a default-value generation function that will take place \n as part of the ``__init__()`` \n method as generated by the dataclass process. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.insert_default` \n \n``` \n:param compare: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be included in comparison operations when generating the \n ``__eq__()`` and ``__ne__()`` methods for the mapped class. \n \n .. versionadded:: 2.0.0b4 \n \n:param kw_only: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be marked as keyword-only when generating the ``__init__()``. \n \n:param hash: Specific to \n `orm_declarative_native_dataclasses`, controls if this field \n is included when generating the ``__hash__()`` method for the mapped \n class. \n \n .. versionadded:: 2.0.36 \n \n:param dataclass_metadata: Specific to \n `orm_declarative_native_dataclasses`, supplies metadata \n to be attached to the generated dataclass field. \n \n .. versionadded:: 2.0.42 \n \n:param \\**kw: All remaining keyword arguments are passed through to the \n constructor for the `_schema.Column`.\n\nGo to [SchemaEventTarget](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L1823,7) | [TypeEngine](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/type_api.py#L116,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [_NoArg](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L118,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [SchemaConst](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L145,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3627,7) | [ColumnElement](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L1287,7) | [FetchedValue](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L4706,7) | [TextClause](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L2545,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L862,7) | [MappedColumn](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/properties.py#L516,7)"}}} +{"suite": "sqlalchemy", "label": "sessionmaker hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 12, "character": 27, "iteration": 4, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(function) mapped_column: def mapped_column[_T](\n __name_pos : SchemaEventTarget | TypeEngine[Any] | str | type[TypeEngine[Any]] | None = None,\n __type_pos : SchemaEventTarget | TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n /,\n *args : SchemaEventTarget,\n *,\n init : _NoArg | bool = _NoArg.NO_ARG,\n repr : _NoArg | bool = _NoArg.NO_ARG,\n default : Any | None = _NoArg.NO_ARG,\n default_factory : (() -> _T) | _NoArg = _NoArg.NO_ARG,\n compare : _NoArg | bool = _NoArg.NO_ARG,\n kw_only : _NoArg | bool = _NoArg.NO_ARG,\n hash : _NoArg | bool | None = _NoArg.NO_ARG,\n nullable : Literal[SchemaConst.NULL_UNSPECIFIED] | bool | None = SchemaConst.NULL_UNSPECIFIED,\n primary_key : bool | None = False,\n deferred : _NoArg | bool = _NoArg.NO_ARG,\n deferred_group : str | None = None,\n deferred_raiseload : bool | None = None,\n use_existing_column: bool = False,\n name : str | None = None,\n type_ : TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n autoincrement : _AutoIncrementType = 'auto',\n doc : str | None = None,\n key : str | None = None,\n index : bool | None = None,\n unique : bool | None = None,\n info : dict[Any, Any] | None = None,\n onupdate : Any | None = None,\n insert_default : Any | None = _NoArg.NO_ARG,\n server_default : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n server_onupdate : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n active_history : bool = False,\n quote : bool | None = None,\n system : bool = False,\n comment : str | None = None,\n sort_order : _NoArg | int = _NoArg.NO_ARG,\n dataclass_metadata : Mapping[Any, Any] | _NoArg | None = _NoArg.NO_ARG,\n **kw : Any\n) -> MappedColumn[Any]: ...\n```\n---\ndeclare a new ORM-mapped `_schema.Column` construct \nfor use within `Declarative Table ` \nconfiguration. \n \nThe `_orm.mapped_column` function provides an ORM-aware and \nPython-typing-compatible construct which is used with \n`declarative ` mappings to indicate an \nattribute that's mapped to a Core `_schema.Column` object. It \nprovides the equivalent feature as mapping an attribute to a \n`_schema.Column` object directly when using Declarative, \nspecifically when using `Declarative Table ` \nconfiguration. \n \n.. versionadded:: 2.0 \n \n`_orm.mapped_column` is normally used with explicit typing along with \nthe `_orm.Mapped` annotation type, where it can derive the SQL \ntype and nullability for the column based on what's present within the \n`_orm.Mapped` annotation. It also may be used without annotations \nas a drop-in replacement for how `_schema.Column` is used in \nDeclarative mappings in SQLAlchemy 1.x style. \n \nFor usage examples of `_orm.mapped_column`, see the documentation \nat `orm_declarative_table`. \n \n.. seealso: \n \n``` \n`orm_declarative_table` - complete documentation \n \n`whatsnew_20_orm_declarative_typing` - migration notes for \nDeclarative mappings using 1.x style mappings \n \n``` \n:param __name: String name to give to the `_schema.Column`. This \n is an optional, positional only argument that if present must be the \n first positional argument passed. If omitted, the attribute name to \n which the `_orm.mapped_column` is mapped will be used as the SQL \n column name. \n:param __type: `_types.TypeEngine` type or instance which will \n indicate the datatype to be associated with the `_schema.Column`. \n This is an optional, positional-only argument that if present must \n immediately follow the ``__name`` parameter if present also, or otherwise \n be the first positional parameter. If omitted, the ultimate type for \n the column may be derived either from the annotated type, or if a \n `_schema.ForeignKey` is present, from the datatype of the \n referenced column. \n:param \\*args: Additional positional arguments include constructs such \n as `_schema.ForeignKey`, `_schema.CheckConstraint`, \n and `_schema.Identity`, which are passed through to the constructed \n `_schema.Column`. \n:param nullable: Optional bool, whether the column should be \"NULL\" or \n \"NOT NULL\". If omitted, the nullability is derived from the type \n annotation based on whether or not ``typing.Optional`` (or its equivalent) \n is present. ``nullable`` defaults to ``True`` otherwise for non-primary \n key columns, and ``False`` for primary key columns. \n:param primary_key: optional bool, indicates the `_schema.Column` \n would be part of the table's primary key or not. \n:param deferred: Optional bool - this keyword argument is consumed by the \n ORM declarative process, and is not part of the `_schema.Column` \n itself; instead, it indicates that this column should be \"deferred\" for \n loading as though mapped by `_orm.deferred`. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_declarative` \n \n``` \n:param deferred_group: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.group` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_group` \n \n``` \n:param deferred_raiseload: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.raiseload` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_raiseload` \n \n``` \n:param use_existing_column: if True, will attempt to locate the given \n column name on an inherited superclass (typically single inheriting \n superclass), and if present, will not produce a new column, mapping \n to the superclass column as though it were omitted from this class. \n This is used for mixins that add new columns to an inherited superclass. \n \n .. seealso: \n \n``` \n`orm_inheritance_column_conflicts` \n \n``` \n .. versionadded:: 2.0.0b4 \n \n:param default: Passed directly to the \n `_schema.Column.default` parameter if the \n `_orm.mapped_column.insert_default` parameter is not present. \n Additionally, when used with `orm_declarative_native_dataclasses`, \n indicates a default Python value that should be applied to the keyword \n constructor within the generated ``__init__()`` method. \n \n Note that in the case of dataclass generation when \n `_orm.mapped_column.insert_default` is not present, this means \n the `_orm.mapped_column.default` value is used in **two** \n places, both the ``__init__()`` method as well as the \n `_schema.Column.default` parameter. While this behavior may \n change in a future release, for the moment this tends to \"work out\"; a \n default of ``None`` will mean that the `_schema.Column` gets no \n default generator, whereas a default that refers to a non-``None`` Python \n or SQL expression value will be assigned up front on the object when \n ``__init__()`` is called, which is the same value that the Core \n `_sql.Insert` construct would use in any case, leading to the same \n end result. \n \n .. note:: When using Core level column defaults that are callables to \n    be interpreted by the underlying `_schema.Column` in conjunction \n    with `ORM-mapped dataclasses \n    `, especially those that are \n    `context-aware default functions `, \n    **the** `_orm.mapped_column.insert_default` **parameter must \n    be used instead**. This is necessary to disambiguate the callable from \n    being interpreted as a dataclass level default. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.insert_default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param insert_default: Passed directly to the \n `_schema.Column.default` parameter; will supersede the value \n of `_orm.mapped_column.default` when present, however \n `_orm.mapped_column.default` will always apply to the \n constructor default for a dataclasses mapping. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param sort_order: An integer that indicates how this mapped column \n should be sorted compared to the others when the ORM is creating a \n `_schema.Table`. Among mapped columns that have the same \n value the default ordering is used, placing first the mapped columns \n defined in the main class, then the ones in the super classes. \n Defaults to 0. The sort is ascending. \n \n .. versionadded:: 2.0.4 \n \n:param active_history=False: \n \n    When ``True``, indicates that the \"previous\" value for a \n    scalar attribute should be loaded when replaced, if not \n    already loaded. Normally, history tracking logic for \n    simple non-primary-key scalar values only needs to be \n    aware of the \"new\" value in order to perform a flush. This \n    flag is available for applications that make use of \n    `.attributes.get_history` or `.Session.is_modified` \n    which also need to know the \"previous\" value of the attribute. \n \n    .. versionadded:: 2.0.10 \n \n \n:param init: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__init__()`` \n method as generated by the dataclass process. \n:param repr: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__repr__()`` \n method as generated by the dataclass process. \n:param default_factory: Specific to \n `orm_declarative_native_dataclasses`, \n specifies a default-value generation function that will take place \n as part of the ``__init__()`` \n method as generated by the dataclass process. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.insert_default` \n \n``` \n:param compare: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be included in comparison operations when generating the \n ``__eq__()`` and ``__ne__()`` methods for the mapped class. \n \n .. versionadded:: 2.0.0b4 \n \n:param kw_only: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be marked as keyword-only when generating the ``__init__()``. \n \n:param hash: Specific to \n `orm_declarative_native_dataclasses`, controls if this field \n is included when generating the ``__hash__()`` method for the mapped \n class. \n \n .. versionadded:: 2.0.36 \n \n:param dataclass_metadata: Specific to \n `orm_declarative_native_dataclasses`, supplies metadata \n to be attached to the generated dataclass field. \n \n .. versionadded:: 2.0.42 \n \n:param \\**kw: All remaining keyword arguments are passed through to the \n constructor for the `_schema.Column`.\n\nGo to [SchemaEventTarget](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L1823,7) | [TypeEngine](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/type_api.py#L116,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [_NoArg](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L118,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [SchemaConst](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L145,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3627,7) | [ColumnElement](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L1287,7) | [FetchedValue](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L4706,7) | [TextClause](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L2545,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L862,7) | [MappedColumn](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/properties.py#L516,7)"}}} +{"suite": "sqlalchemy", "label": "sessionmaker hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 12, "character": 27, "iteration": 5, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(function) mapped_column: def mapped_column[_T](\n __name_pos : SchemaEventTarget | TypeEngine[Any] | str | type[TypeEngine[Any]] | None = None,\n __type_pos : SchemaEventTarget | TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n /,\n *args : SchemaEventTarget,\n *,\n init : _NoArg | bool = _NoArg.NO_ARG,\n repr : _NoArg | bool = _NoArg.NO_ARG,\n default : Any | None = _NoArg.NO_ARG,\n default_factory : (() -> _T) | _NoArg = _NoArg.NO_ARG,\n compare : _NoArg | bool = _NoArg.NO_ARG,\n kw_only : _NoArg | bool = _NoArg.NO_ARG,\n hash : _NoArg | bool | None = _NoArg.NO_ARG,\n nullable : Literal[SchemaConst.NULL_UNSPECIFIED] | bool | None = SchemaConst.NULL_UNSPECIFIED,\n primary_key : bool | None = False,\n deferred : _NoArg | bool = _NoArg.NO_ARG,\n deferred_group : str | None = None,\n deferred_raiseload : bool | None = None,\n use_existing_column: bool = False,\n name : str | None = None,\n type_ : TypeEngine[Any] | type[TypeEngine[Any]] | None = None,\n autoincrement : _AutoIncrementType = 'auto',\n doc : str | None = None,\n key : str | None = None,\n index : bool | None = None,\n unique : bool | None = None,\n info : dict[Any, Any] | None = None,\n onupdate : Any | None = None,\n insert_default : Any | None = _NoArg.NO_ARG,\n server_default : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n server_onupdate : ColumnElement[Any] | FetchedValue | TextClause | str | None = None,\n active_history : bool = False,\n quote : bool | None = None,\n system : bool = False,\n comment : str | None = None,\n sort_order : _NoArg | int = _NoArg.NO_ARG,\n dataclass_metadata : Mapping[Any, Any] | _NoArg | None = _NoArg.NO_ARG,\n **kw : Any\n) -> MappedColumn[Any]: ...\n```\n---\ndeclare a new ORM-mapped `_schema.Column` construct \nfor use within `Declarative Table ` \nconfiguration. \n \nThe `_orm.mapped_column` function provides an ORM-aware and \nPython-typing-compatible construct which is used with \n`declarative ` mappings to indicate an \nattribute that's mapped to a Core `_schema.Column` object. It \nprovides the equivalent feature as mapping an attribute to a \n`_schema.Column` object directly when using Declarative, \nspecifically when using `Declarative Table ` \nconfiguration. \n \n.. versionadded:: 2.0 \n \n`_orm.mapped_column` is normally used with explicit typing along with \nthe `_orm.Mapped` annotation type, where it can derive the SQL \ntype and nullability for the column based on what's present within the \n`_orm.Mapped` annotation. It also may be used without annotations \nas a drop-in replacement for how `_schema.Column` is used in \nDeclarative mappings in SQLAlchemy 1.x style. \n \nFor usage examples of `_orm.mapped_column`, see the documentation \nat `orm_declarative_table`. \n \n.. seealso: \n \n``` \n`orm_declarative_table` - complete documentation \n \n`whatsnew_20_orm_declarative_typing` - migration notes for \nDeclarative mappings using 1.x style mappings \n \n``` \n:param __name: String name to give to the `_schema.Column`. This \n is an optional, positional only argument that if present must be the \n first positional argument passed. If omitted, the attribute name to \n which the `_orm.mapped_column` is mapped will be used as the SQL \n column name. \n:param __type: `_types.TypeEngine` type or instance which will \n indicate the datatype to be associated with the `_schema.Column`. \n This is an optional, positional-only argument that if present must \n immediately follow the ``__name`` parameter if present also, or otherwise \n be the first positional parameter. If omitted, the ultimate type for \n the column may be derived either from the annotated type, or if a \n `_schema.ForeignKey` is present, from the datatype of the \n referenced column. \n:param \\*args: Additional positional arguments include constructs such \n as `_schema.ForeignKey`, `_schema.CheckConstraint`, \n and `_schema.Identity`, which are passed through to the constructed \n `_schema.Column`. \n:param nullable: Optional bool, whether the column should be \"NULL\" or \n \"NOT NULL\". If omitted, the nullability is derived from the type \n annotation based on whether or not ``typing.Optional`` (or its equivalent) \n is present. ``nullable`` defaults to ``True`` otherwise for non-primary \n key columns, and ``False`` for primary key columns. \n:param primary_key: optional bool, indicates the `_schema.Column` \n would be part of the table's primary key or not. \n:param deferred: Optional bool - this keyword argument is consumed by the \n ORM declarative process, and is not part of the `_schema.Column` \n itself; instead, it indicates that this column should be \"deferred\" for \n loading as though mapped by `_orm.deferred`. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_declarative` \n \n``` \n:param deferred_group: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.group` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_group` \n \n``` \n:param deferred_raiseload: Implies `_orm.mapped_column.deferred` \n to ``True``, and set the `_orm.deferred.raiseload` parameter. \n \n .. seealso: \n \n``` \n`orm_queryguide_deferred_raiseload` \n \n``` \n:param use_existing_column: if True, will attempt to locate the given \n column name on an inherited superclass (typically single inheriting \n superclass), and if present, will not produce a new column, mapping \n to the superclass column as though it were omitted from this class. \n This is used for mixins that add new columns to an inherited superclass. \n \n .. seealso: \n \n``` \n`orm_inheritance_column_conflicts` \n \n``` \n .. versionadded:: 2.0.0b4 \n \n:param default: Passed directly to the \n `_schema.Column.default` parameter if the \n `_orm.mapped_column.insert_default` parameter is not present. \n Additionally, when used with `orm_declarative_native_dataclasses`, \n indicates a default Python value that should be applied to the keyword \n constructor within the generated ``__init__()`` method. \n \n Note that in the case of dataclass generation when \n `_orm.mapped_column.insert_default` is not present, this means \n the `_orm.mapped_column.default` value is used in **two** \n places, both the ``__init__()`` method as well as the \n `_schema.Column.default` parameter. While this behavior may \n change in a future release, for the moment this tends to \"work out\"; a \n default of ``None`` will mean that the `_schema.Column` gets no \n default generator, whereas a default that refers to a non-``None`` Python \n or SQL expression value will be assigned up front on the object when \n ``__init__()`` is called, which is the same value that the Core \n `_sql.Insert` construct would use in any case, leading to the same \n end result. \n \n .. note:: When using Core level column defaults that are callables to \n    be interpreted by the underlying `_schema.Column` in conjunction \n    with `ORM-mapped dataclasses \n    `, especially those that are \n    `context-aware default functions `, \n    **the** `_orm.mapped_column.insert_default` **parameter must \n    be used instead**. This is necessary to disambiguate the callable from \n    being interpreted as a dataclass level default. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.insert_default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param insert_default: Passed directly to the \n `_schema.Column.default` parameter; will supersede the value \n of `_orm.mapped_column.default` when present, however \n `_orm.mapped_column.default` will always apply to the \n constructor default for a dataclasses mapping. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.default_factory` \n \n``` \n:param sort_order: An integer that indicates how this mapped column \n should be sorted compared to the others when the ORM is creating a \n `_schema.Table`. Among mapped columns that have the same \n value the default ordering is used, placing first the mapped columns \n defined in the main class, then the ones in the super classes. \n Defaults to 0. The sort is ascending. \n \n .. versionadded:: 2.0.4 \n \n:param active_history=False: \n \n    When ``True``, indicates that the \"previous\" value for a \n    scalar attribute should be loaded when replaced, if not \n    already loaded. Normally, history tracking logic for \n    simple non-primary-key scalar values only needs to be \n    aware of the \"new\" value in order to perform a flush. This \n    flag is available for applications that make use of \n    `.attributes.get_history` or `.Session.is_modified` \n    which also need to know the \"previous\" value of the attribute. \n \n    .. versionadded:: 2.0.10 \n \n \n:param init: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__init__()`` \n method as generated by the dataclass process. \n:param repr: Specific to `orm_declarative_native_dataclasses`, \n specifies if the mapped attribute should be part of the ``__repr__()`` \n method as generated by the dataclass process. \n:param default_factory: Specific to \n `orm_declarative_native_dataclasses`, \n specifies a default-value generation function that will take place \n as part of the ``__init__()`` \n method as generated by the dataclass process. \n \n .. seealso: \n \n``` \n`defaults_default_factory_insert_default` \n \n`_orm.mapped_column.default` \n \n`_orm.mapped_column.insert_default` \n \n``` \n:param compare: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be included in comparison operations when generating the \n ``__eq__()`` and ``__ne__()`` methods for the mapped class. \n \n .. versionadded:: 2.0.0b4 \n \n:param kw_only: Specific to \n `orm_declarative_native_dataclasses`, indicates if this field \n should be marked as keyword-only when generating the ``__init__()``. \n \n:param hash: Specific to \n `orm_declarative_native_dataclasses`, controls if this field \n is included when generating the ``__hash__()`` method for the mapped \n class. \n \n .. versionadded:: 2.0.36 \n \n:param dataclass_metadata: Specific to \n `orm_declarative_native_dataclasses`, supplies metadata \n to be attached to the generated dataclass field. \n \n .. versionadded:: 2.0.42 \n \n:param \\**kw: All remaining keyword arguments are passed through to the \n constructor for the `_schema.Column`.\n\nGo to [SchemaEventTarget](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L1823,7) | [TypeEngine](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/type_api.py#L116,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [_NoArg](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/base.py#L118,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [SchemaConst](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L145,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3627,7) | [ColumnElement](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L1287,7) | [FetchedValue](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/schema.py#L4706,7) | [TextClause](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/elements.py#L2545,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L862,7) | [MappedColumn](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/properties.py#L516,7)"}}} {"suite": "sqlalchemy", "label": "mapped class definition", "method": "textDocument/definition", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 16, "character": 16, "iteration": 1, "result": {"range": {"end": {"character": 16, "line": 5317}, "start": {"character": 8, "line": 5317}}, "uri": "file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/session.py"}} {"suite": "sqlalchemy", "label": "mapped class definition", "method": "textDocument/definition", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 16, "character": 16, "iteration": 2, "result": {"range": {"end": {"character": 16, "line": 5317}, "start": {"character": 8, "line": 5317}}, "uri": "file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/session.py"}} {"suite": "sqlalchemy", "label": "mapped class definition", "method": "textDocument/definition", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 16, "character": 16, "iteration": 3, "result": {"range": {"end": {"character": 16, "line": 5317}, "start": {"character": 8, "line": 5317}}, "uri": "file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/session.py"}} @@ -93,11 +93,11 @@ {"suite": "sqlalchemy", "label": "edit query then complete (edit+completion)", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 18, "character": 51, "iteration": 3, "result": {"isIncomplete": false, "items": [{"detail": "int", "kind": 5, "label": "id", "sortText": "0.9999.id"}, {"detail": "MetaData", "kind": 5, "label": "metadata", "sortText": "0.9999.metadata"}, {"detail": "str", "kind": 5, "label": "name", "sortText": "0.9999.name"}, {"detail": "registry", "kind": 5, "label": "registry", "sortText": "0.9999.registry"}, {"detail": "def _sa_inspect_instance(self: User) -> InstanceState[User]: ...", "kind": 2, "label": "_sa_inspect_instance", "sortText": "2.9999._sa_inspect_instance"}, {"detail": "def _sa_inspect_type(self: User) -> Mapper[User]: ...", "kind": 2, "label": "_sa_inspect_type", "sortText": "2.9999._sa_inspect_type"}, {"detail": "registry", "kind": 5, "label": "_sa_registry", "sortText": "2.9999._sa_registry"}, {"detail": "def __bool__(self: NoneType) -> Literal[False]: ...", "documentation": {"kind": "markdown", "value": "True if self else False"}, "kind": 2, "label": "__bool__", "sortText": "3.9999.__bool__"}, {"detail": "((self: NoneType) -> None) | ((\n self: User,\n **kw: Any\n) -> None)", "kind": 5, "label": "__init__", "sortText": "3.9999.__init__"}, {"detail": "((cls: type[NoneType]) -> None) | ((\n cls: type[User],\n **kw: Any\n) -> None)", "kind": 5, "label": "__init_subclass__", "sortText": "3.9999.__init_subclass__"}, {"detail": "Mapper[Any]", "kind": 5, "label": "__mapper__", "sortText": "3.9999.__mapper__"}, {"detail": "Any", "kind": 5, "label": "__mapper_args__", "sortText": "3.9999.__mapper_args__"}, {"detail": "str", "kind": 5, "label": "__name__", "sortText": "3.9999.__name__"}, {"detail": "tuple[()]", "kind": 5, "label": "__slots__", "sortText": "3.9999.__slots__"}, {"detail": "FromClause", "kind": 5, "label": "__table__", "sortText": "3.9999.__table__"}, {"detail": "Any", "kind": 5, "label": "__table_args__", "sortText": "3.9999.__table_args__"}, {"detail": "Any", "kind": 5, "label": "__tablename__", "sortText": "3.9999.__tablename__"}]}} {"suite": "sqlalchemy", "label": "edit query then complete (edit+completion)", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 18, "character": 51, "iteration": 4, "result": {"isIncomplete": false, "items": [{"detail": "int", "kind": 5, "label": "id", "sortText": "0.9999.id"}, {"detail": "MetaData", "kind": 5, "label": "metadata", "sortText": "0.9999.metadata"}, {"detail": "str", "kind": 5, "label": "name", "sortText": "0.9999.name"}, {"detail": "registry", "kind": 5, "label": "registry", "sortText": "0.9999.registry"}, {"detail": "def _sa_inspect_instance(self: User) -> InstanceState[User]: ...", "kind": 2, "label": "_sa_inspect_instance", "sortText": "2.9999._sa_inspect_instance"}, {"detail": "def _sa_inspect_type(self: User) -> Mapper[User]: ...", "kind": 2, "label": "_sa_inspect_type", "sortText": "2.9999._sa_inspect_type"}, {"detail": "registry", "kind": 5, "label": "_sa_registry", "sortText": "2.9999._sa_registry"}, {"detail": "def __bool__(self: NoneType) -> Literal[False]: ...", "documentation": {"kind": "markdown", "value": "True if self else False"}, "kind": 2, "label": "__bool__", "sortText": "3.9999.__bool__"}, {"detail": "((self: NoneType) -> None) | ((\n self: User,\n **kw: Any\n) -> None)", "kind": 5, "label": "__init__", "sortText": "3.9999.__init__"}, {"detail": "((cls: type[NoneType]) -> None) | ((\n cls: type[User],\n **kw: Any\n) -> None)", "kind": 5, "label": "__init_subclass__", "sortText": "3.9999.__init_subclass__"}, {"detail": "Mapper[Any]", "kind": 5, "label": "__mapper__", "sortText": "3.9999.__mapper__"}, {"detail": "Any", "kind": 5, "label": "__mapper_args__", "sortText": "3.9999.__mapper_args__"}, {"detail": "str", "kind": 5, "label": "__name__", "sortText": "3.9999.__name__"}, {"detail": "tuple[()]", "kind": 5, "label": "__slots__", "sortText": "3.9999.__slots__"}, {"detail": "FromClause", "kind": 5, "label": "__table__", "sortText": "3.9999.__table__"}, {"detail": "Any", "kind": 5, "label": "__table_args__", "sortText": "3.9999.__table_args__"}, {"detail": "Any", "kind": 5, "label": "__tablename__", "sortText": "3.9999.__tablename__"}]}} {"suite": "sqlalchemy", "label": "edit query then complete (edit+completion)", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 18, "character": 51, "iteration": 5, "result": {"isIncomplete": false, "items": [{"detail": "int", "kind": 5, "label": "id", "sortText": "0.9999.id"}, {"detail": "MetaData", "kind": 5, "label": "metadata", "sortText": "0.9999.metadata"}, {"detail": "str", "kind": 5, "label": "name", "sortText": "0.9999.name"}, {"detail": "registry", "kind": 5, "label": "registry", "sortText": "0.9999.registry"}, {"detail": "def _sa_inspect_instance(self: User) -> InstanceState[User]: ...", "kind": 2, "label": "_sa_inspect_instance", "sortText": "2.9999._sa_inspect_instance"}, {"detail": "def _sa_inspect_type(self: User) -> Mapper[User]: ...", "kind": 2, "label": "_sa_inspect_type", "sortText": "2.9999._sa_inspect_type"}, {"detail": "registry", "kind": 5, "label": "_sa_registry", "sortText": "2.9999._sa_registry"}, {"detail": "def __bool__(self: NoneType) -> Literal[False]: ...", "documentation": {"kind": "markdown", "value": "True if self else False"}, "kind": 2, "label": "__bool__", "sortText": "3.9999.__bool__"}, {"detail": "((self: NoneType) -> None) | ((\n self: User,\n **kw: Any\n) -> None)", "kind": 5, "label": "__init__", "sortText": "3.9999.__init__"}, {"detail": "((cls: type[NoneType]) -> None) | ((\n cls: type[User],\n **kw: Any\n) -> None)", "kind": 5, "label": "__init_subclass__", "sortText": "3.9999.__init_subclass__"}, {"detail": "Mapper[Any]", "kind": 5, "label": "__mapper__", "sortText": "3.9999.__mapper__"}, {"detail": "Any", "kind": 5, "label": "__mapper_args__", "sortText": "3.9999.__mapper_args__"}, {"detail": "str", "kind": 5, "label": "__name__", "sortText": "3.9999.__name__"}, {"detail": "tuple[()]", "kind": 5, "label": "__slots__", "sortText": "3.9999.__slots__"}, {"detail": "FromClause", "kind": 5, "label": "__table__", "sortText": "3.9999.__table__"}, {"detail": "Any", "kind": 5, "label": "__table_args__", "sortText": "3.9999.__table_args__"}, {"detail": "Any", "kind": 5, "label": "__tablename__", "sortText": "3.9999.__tablename__"}]}} -{"suite": "sqlalchemy", "label": "edit session then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 18, "character": 24, "iteration": 1, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) scalars: [_T: Any](\n self : Session,\n statement : TypedReturnsRows[_T],\n params : Mapping[str, Any] | Sequence[Mapping[str, Any]] | None = None,\n *,\n execution_options: OrmExecuteOptionsParameter = ...,\n bind_arguments : dict[str, Any] | None = None,\n **kw : Any\n) -> ScalarResult[_T]\n```\n---\nExecute a statement and return the results as scalars. \n \nUsage and parameters are the same as that of \n`_orm.Session.execute`; the return result is a \n`_result.ScalarResult` filtering object which \nwill return single elements rather than `_row.Row` objects. \n \n:return: a `_result.ScalarResult` object \n \n.. versionadded:: 1.4.24 Added `_orm.Session.scalars` \n \n.. versionadded:: 1.4.26 Added `_orm.scoped_session.scalars` \n \n.. seealso: \n \n``` \n`orm_queryguide_select_orm_entities` - contrasts the behavior \nof `_orm.Session.execute` to `_orm.Session.scalars` \n \n \n```\n\nGo to [Session](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/session.py#L1472,7) | [TypedReturnsRows](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/selectable.py#L311,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L862,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [_OrmKnownExecutionOptions](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/_typing.py#L86,7) | [immutabledict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/util/_immutabledict_cy.py#L110,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3627,7) | [ScalarResult](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/engine/result.py#L1293,7)"}}} -{"suite": "sqlalchemy", "label": "edit session then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 18, "character": 24, "iteration": 2, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) scalars: [_T: Any](\n self : Session,\n statement : TypedReturnsRows[_T],\n params : Mapping[str, Any] | Sequence[Mapping[str, Any]] | None = None,\n *,\n execution_options: OrmExecuteOptionsParameter = ...,\n bind_arguments : dict[str, Any] | None = None,\n **kw : Any\n) -> ScalarResult[_T]\n```\n---\nExecute a statement and return the results as scalars. \n \nUsage and parameters are the same as that of \n`_orm.Session.execute`; the return result is a \n`_result.ScalarResult` filtering object which \nwill return single elements rather than `_row.Row` objects. \n \n:return: a `_result.ScalarResult` object \n \n.. versionadded:: 1.4.24 Added `_orm.Session.scalars` \n \n.. versionadded:: 1.4.26 Added `_orm.scoped_session.scalars` \n \n.. seealso: \n \n``` \n`orm_queryguide_select_orm_entities` - contrasts the behavior \nof `_orm.Session.execute` to `_orm.Session.scalars` \n \n \n```\n\nGo to [Session](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/session.py#L1472,7) | [TypedReturnsRows](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/selectable.py#L311,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L862,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [_OrmKnownExecutionOptions](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/_typing.py#L86,7) | [immutabledict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/util/_immutabledict_cy.py#L110,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3627,7) | [ScalarResult](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/engine/result.py#L1293,7)"}}} -{"suite": "sqlalchemy", "label": "edit session then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 18, "character": 24, "iteration": 3, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) scalars: [_T: Any](\n self : Session,\n statement : TypedReturnsRows[_T],\n params : Mapping[str, Any] | Sequence[Mapping[str, Any]] | None = None,\n *,\n execution_options: OrmExecuteOptionsParameter = ...,\n bind_arguments : dict[str, Any] | None = None,\n **kw : Any\n) -> ScalarResult[_T]\n```\n---\nExecute a statement and return the results as scalars. \n \nUsage and parameters are the same as that of \n`_orm.Session.execute`; the return result is a \n`_result.ScalarResult` filtering object which \nwill return single elements rather than `_row.Row` objects. \n \n:return: a `_result.ScalarResult` object \n \n.. versionadded:: 1.4.24 Added `_orm.Session.scalars` \n \n.. versionadded:: 1.4.26 Added `_orm.scoped_session.scalars` \n \n.. seealso: \n \n``` \n`orm_queryguide_select_orm_entities` - contrasts the behavior \nof `_orm.Session.execute` to `_orm.Session.scalars` \n \n \n```\n\nGo to [Session](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/session.py#L1472,7) | [TypedReturnsRows](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/selectable.py#L311,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L862,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [_OrmKnownExecutionOptions](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/_typing.py#L86,7) | [immutabledict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/util/_immutabledict_cy.py#L110,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3627,7) | [ScalarResult](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/engine/result.py#L1293,7)"}}} -{"suite": "sqlalchemy", "label": "edit session then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 18, "character": 24, "iteration": 4, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) scalars: [_T: Any](\n self : Session,\n statement : TypedReturnsRows[_T],\n params : Mapping[str, Any] | Sequence[Mapping[str, Any]] | None = None,\n *,\n execution_options: OrmExecuteOptionsParameter = ...,\n bind_arguments : dict[str, Any] | None = None,\n **kw : Any\n) -> ScalarResult[_T]\n```\n---\nExecute a statement and return the results as scalars. \n \nUsage and parameters are the same as that of \n`_orm.Session.execute`; the return result is a \n`_result.ScalarResult` filtering object which \nwill return single elements rather than `_row.Row` objects. \n \n:return: a `_result.ScalarResult` object \n \n.. versionadded:: 1.4.24 Added `_orm.Session.scalars` \n \n.. versionadded:: 1.4.26 Added `_orm.scoped_session.scalars` \n \n.. seealso: \n \n``` \n`orm_queryguide_select_orm_entities` - contrasts the behavior \nof `_orm.Session.execute` to `_orm.Session.scalars` \n \n \n```\n\nGo to [Session](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/session.py#L1472,7) | [TypedReturnsRows](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/selectable.py#L311,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L862,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [_OrmKnownExecutionOptions](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/_typing.py#L86,7) | [immutabledict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/util/_immutabledict_cy.py#L110,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3627,7) | [ScalarResult](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/engine/result.py#L1293,7)"}}} -{"suite": "sqlalchemy", "label": "edit session then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 18, "character": 24, "iteration": 5, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) scalars: [_T: Any](\n self : Session,\n statement : TypedReturnsRows[_T],\n params : Mapping[str, Any] | Sequence[Mapping[str, Any]] | None = None,\n *,\n execution_options: OrmExecuteOptionsParameter = ...,\n bind_arguments : dict[str, Any] | None = None,\n **kw : Any\n) -> ScalarResult[_T]\n```\n---\nExecute a statement and return the results as scalars. \n \nUsage and parameters are the same as that of \n`_orm.Session.execute`; the return result is a \n`_result.ScalarResult` filtering object which \nwill return single elements rather than `_row.Row` objects. \n \n:return: a `_result.ScalarResult` object \n \n.. versionadded:: 1.4.24 Added `_orm.Session.scalars` \n \n.. versionadded:: 1.4.26 Added `_orm.scoped_session.scalars` \n \n.. seealso: \n \n``` \n`orm_queryguide_select_orm_entities` - contrasts the behavior \nof `_orm.Session.execute` to `_orm.Session.scalars` \n \n \n```\n\nGo to [Session](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/session.py#L1472,7) | [TypedReturnsRows](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/selectable.py#L311,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L862,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L718,7) | [_OrmKnownExecutionOptions](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/_typing.py#L86,7) | [immutabledict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/util/_immutabledict_cy.py#L110,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3627,7) | [ScalarResult](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/engine/result.py#L1293,7)"}}} +{"suite": "sqlalchemy", "label": "edit session then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 18, "character": 24, "iteration": 1, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) scalars: [_T: Any](\n self : Session,\n statement : TypedReturnsRows[_T],\n params : Mapping[str, Any] | Sequence[Mapping[str, Any]] | None = None,\n *,\n execution_options: OrmExecuteOptionsParameter = ...,\n bind_arguments : dict[str, Any] | None = None,\n **kw : Any\n) -> ScalarResult[_T]\n```\n---\nExecute a statement and return the results as scalars. \n \nUsage and parameters are the same as that of \n`_orm.Session.execute`; the return result is a \n`_result.ScalarResult` filtering object which \nwill return single elements rather than `_row.Row` objects. \n \n:return: a `_result.ScalarResult` object \n \n.. versionadded:: 1.4.24 Added `_orm.Session.scalars` \n \n.. versionadded:: 1.4.26 Added `_orm.scoped_session.scalars` \n \n.. seealso: \n \n``` \n`orm_queryguide_select_orm_entities` - contrasts the behavior \nof `_orm.Session.execute` to `_orm.Session.scalars` \n \n \n```\n\nGo to [Session](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/session.py#L1472,7) | [TypedReturnsRows](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/selectable.py#L311,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L862,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [_OrmKnownExecutionOptions](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/_typing.py#L86,7) | [immutabledict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/util/_immutabledict_cy.py#L110,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3627,7) | [ScalarResult](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/engine/result.py#L1293,7)"}}} +{"suite": "sqlalchemy", "label": "edit session then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 18, "character": 24, "iteration": 2, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) scalars: [_T: Any](\n self : Session,\n statement : TypedReturnsRows[_T],\n params : Mapping[str, Any] | Sequence[Mapping[str, Any]] | None = None,\n *,\n execution_options: OrmExecuteOptionsParameter = ...,\n bind_arguments : dict[str, Any] | None = None,\n **kw : Any\n) -> ScalarResult[_T]\n```\n---\nExecute a statement and return the results as scalars. \n \nUsage and parameters are the same as that of \n`_orm.Session.execute`; the return result is a \n`_result.ScalarResult` filtering object which \nwill return single elements rather than `_row.Row` objects. \n \n:return: a `_result.ScalarResult` object \n \n.. versionadded:: 1.4.24 Added `_orm.Session.scalars` \n \n.. versionadded:: 1.4.26 Added `_orm.scoped_session.scalars` \n \n.. seealso: \n \n``` \n`orm_queryguide_select_orm_entities` - contrasts the behavior \nof `_orm.Session.execute` to `_orm.Session.scalars` \n \n \n```\n\nGo to [Session](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/session.py#L1472,7) | [TypedReturnsRows](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/selectable.py#L311,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L862,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [_OrmKnownExecutionOptions](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/_typing.py#L86,7) | [immutabledict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/util/_immutabledict_cy.py#L110,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3627,7) | [ScalarResult](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/engine/result.py#L1293,7)"}}} +{"suite": "sqlalchemy", "label": "edit session then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 18, "character": 24, "iteration": 3, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) scalars: [_T: Any](\n self : Session,\n statement : TypedReturnsRows[_T],\n params : Mapping[str, Any] | Sequence[Mapping[str, Any]] | None = None,\n *,\n execution_options: OrmExecuteOptionsParameter = ...,\n bind_arguments : dict[str, Any] | None = None,\n **kw : Any\n) -> ScalarResult[_T]\n```\n---\nExecute a statement and return the results as scalars. \n \nUsage and parameters are the same as that of \n`_orm.Session.execute`; the return result is a \n`_result.ScalarResult` filtering object which \nwill return single elements rather than `_row.Row` objects. \n \n:return: a `_result.ScalarResult` object \n \n.. versionadded:: 1.4.24 Added `_orm.Session.scalars` \n \n.. versionadded:: 1.4.26 Added `_orm.scoped_session.scalars` \n \n.. seealso: \n \n``` \n`orm_queryguide_select_orm_entities` - contrasts the behavior \nof `_orm.Session.execute` to `_orm.Session.scalars` \n \n \n```\n\nGo to [Session](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/session.py#L1472,7) | [TypedReturnsRows](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/selectable.py#L311,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L862,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [_OrmKnownExecutionOptions](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/_typing.py#L86,7) | [immutabledict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/util/_immutabledict_cy.py#L110,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3627,7) | [ScalarResult](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/engine/result.py#L1293,7)"}}} +{"suite": "sqlalchemy", "label": "edit session then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 18, "character": 24, "iteration": 4, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) scalars: [_T: Any](\n self : Session,\n statement : TypedReturnsRows[_T],\n params : Mapping[str, Any] | Sequence[Mapping[str, Any]] | None = None,\n *,\n execution_options: OrmExecuteOptionsParameter = ...,\n bind_arguments : dict[str, Any] | None = None,\n **kw : Any\n) -> ScalarResult[_T]\n```\n---\nExecute a statement and return the results as scalars. \n \nUsage and parameters are the same as that of \n`_orm.Session.execute`; the return result is a \n`_result.ScalarResult` filtering object which \nwill return single elements rather than `_row.Row` objects. \n \n:return: a `_result.ScalarResult` object \n \n.. versionadded:: 1.4.24 Added `_orm.Session.scalars` \n \n.. versionadded:: 1.4.26 Added `_orm.scoped_session.scalars` \n \n.. seealso: \n \n``` \n`orm_queryguide_select_orm_entities` - contrasts the behavior \nof `_orm.Session.execute` to `_orm.Session.scalars` \n \n \n```\n\nGo to [Session](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/session.py#L1472,7) | [TypedReturnsRows](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/selectable.py#L311,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L862,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [_OrmKnownExecutionOptions](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/_typing.py#L86,7) | [immutabledict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/util/_immutabledict_cy.py#L110,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3627,7) | [ScalarResult](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/engine/result.py#L1293,7)"}}} +{"suite": "sqlalchemy", "label": "edit session then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 18, "character": 24, "iteration": 5, "result": {"canIncreaseVerbosity": true, "contents": {"kind": "markdown", "value": "```python\n(method) scalars: [_T: Any](\n self : Session,\n statement : TypedReturnsRows[_T],\n params : Mapping[str, Any] | Sequence[Mapping[str, Any]] | None = None,\n *,\n execution_options: OrmExecuteOptionsParameter = ...,\n bind_arguments : dict[str, Any] | None = None,\n **kw : Any\n) -> ScalarResult[_T]\n```\n---\nExecute a statement and return the results as scalars. \n \nUsage and parameters are the same as that of \n`_orm.Session.execute`; the return result is a \n`_result.ScalarResult` filtering object which \nwill return single elements rather than `_row.Row` objects. \n \n:return: a `_result.ScalarResult` object \n \n.. versionadded:: 1.4.24 Added `_orm.Session.scalars` \n \n.. versionadded:: 1.4.26 Added `_orm.scoped_session.scalars` \n \n.. seealso: \n \n``` \n`orm_queryguide_select_orm_entities` - contrasts the behavior \nof `_orm.Session.execute` to `_orm.Session.scalars` \n \n \n```\n\nGo to [Session](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/session.py#L1472,7) | [TypedReturnsRows](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/sql/selectable.py#L311,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L862,7) | [Sequence](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L718,7) | [_OrmKnownExecutionOptions](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/orm/_typing.py#L86,7) | [immutabledict](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/util/_immutabledict_cy.py#L110,7) | [dict](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3627,7) | [ScalarResult](file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/.venv/lib/python3.12/site-packages/sqlalchemy/engine/result.py#L1293,7)"}}} {"suite": "transformers", "label": "classifier pipeline completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/transformers/src/inference.py", "line": 15, "character": 19, "iteration": 1, "result": {"isIncomplete": true, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"detail": "Unknown", "kind": 6, "label": "classifier", "sortText": "0.9999.classifier"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}]}} {"suite": "transformers", "label": "classifier pipeline completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/transformers/src/inference.py", "line": 15, "character": 19, "iteration": 2, "result": {"isIncomplete": true, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"detail": "Unknown", "kind": 6, "label": "classifier", "sortText": "0.9999.classifier"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}]}} {"suite": "transformers", "label": "classifier pipeline completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/transformers/src/inference.py", "line": 15, "character": 19, "iteration": 3, "result": {"isIncomplete": true, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"detail": "Unknown", "kind": 6, "label": "classifier", "sortText": "0.9999.classifier"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}]}} @@ -128,36 +128,36 @@ {"suite": "tsp_core", "label": "builtins semantic tokens", "method": "typeServer/semanticTokens", "file_path": 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21, "label": "__dict__", "sortText": "3.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE\n", "kind": 6, "label": "ALERT_DESCRIPTION_BAD_CERTIFICATE", "sortText": "4a.9999.ALERT_DESCRIPTION_BAD_CERTIFICATE"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE\n", "kind": 6, "label": "ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE", "sortText": "4a.9999.ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE\n", "kind": 6, "label": "ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE", "sortText": "4a.9999.ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_EXPIRED\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_EXPIRED\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_EXPIRED", "sortText": "4a.9999.ALERT_DESCRIPTION_CERTIFICATE_EXPIRED"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_REVOKED\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_REVOKED\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_REVOKED", "sortText": "4a.9999.ALERT_DESCRIPTION_CERTIFICATE_REVOKED"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN", "sortText": "4a.9999.ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE", "sortText": "4a.9999.ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_CLOSE_NOTIFY\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_CLOSE_NOTIFY\n", "kind": 6, "label": "ALERT_DESCRIPTION_CLOSE_NOTIFY", "sortText": "4a.9999.ALERT_DESCRIPTION_CLOSE_NOTIFY"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_DECRYPT_ERROR\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_DECRYPT_ERROR\n", "kind": 6, "label": "ALERT_DESCRIPTION_DECRYPT_ERROR", "sortText": "4a.9999.ALERT_DESCRIPTION_DECRYPT_ERROR"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_INSUFFICIENT_SECURITY\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_INSUFFICIENT_SECURITY\n", "kind": 6, "label": "ALERT_DESCRIPTION_INSUFFICIENT_SECURITY", "sortText": "4a.9999.ALERT_DESCRIPTION_INSUFFICIENT_SECURITY"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_UNEXPECTED_MESSAGE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_UNEXPECTED_MESSAGE\n", "kind": 6, "label": "ALERT_DESCRIPTION_UNEXPECTED_MESSAGE", "sortText": "4a.9999.ALERT_DESCRIPTION_UNEXPECTED_MESSAGE"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE\n", "kind": 6, "label": "ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE", "sortText": "4a.9999.ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE"}, {"additionalTextEdits": [{"newText": "from urllib3.exceptions import BodyNotHttplibCompatible\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.exceptions import BodyNotHttplibCompatible\n", "kind": 7, "label": "BodyNotHttplibCompatible", "sortText": 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"4a.9999.CallbackDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import CallbackDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import CallbackDict\n", "kind": 7, "label": "CallbackDict", "sortText": "4a.9999.CallbackDict"}, {"additionalTextEdits": [{"newText": "from requests.structures import CaseInsensitiveDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.structures import CaseInsensitiveDict\n", "kind": 7, "label": "CaseInsensitiveDict", "sortText": "4a.9999.CaseInsensitiveDict"}, {"additionalTextEdits": [{"newText": "from requests.utils import CaseInsensitiveDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import CaseInsensitiveDict\n", "kind": 6, "label": "CaseInsensitiveDict", "sortText": "4a.9999.CaseInsensitiveDict"}, {"additionalTextEdits": [{"newText": "from click.types import ChoiceInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import ChoiceInfoDict\n", "kind": 7, "label": "ChoiceInfoDict", "sortText": "4a.9999.ChoiceInfoDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.exceptions import ClientDisconnected\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.exceptions import ClientDisconnected\n", "kind": 7, "label": "ClientDisconnected", "sortText": "4a.9999.ClientDisconnected"}, {"additionalTextEdits": [{"newText": "from werkzeug.test import ClientRedirectError\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.test import ClientRedirectError\n", "kind": 7, "label": "ClientRedirectError", "sortText": "4a.9999.ClientRedirectError"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import CombinedMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import CombinedMultiDict\n", "kind": 7, "label": "CombinedMultiDict", "sortText": "4a.9999.CombinedMultiDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import CombinedMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import CombinedMultiDict\n", "kind": 7, "label": "CombinedMultiDict", "sortText": "4a.9999.CombinedMultiDict"}, {"additionalTextEdits": [{"newText": "from requests.models import DEFAULT_REDIRECT_LIMIT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.models import DEFAULT_REDIRECT_LIMIT\n", "kind": 6, "label": "DEFAULT_REDIRECT_LIMIT", "sortText": 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DebuggedApplication\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.debug import DebuggedApplication\n", "kind": 7, "label": "DebuggedApplication", "sortText": "4a.9999.DebuggedApplication"}, {"additionalTextEdits": [{"newText": "from decimal import DecimalException\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from decimal import DecimalException\n", "kind": 7, "label": "DecimalException", "sortText": "4a.9999.DecimalException"}, {"additionalTextEdits": [{"newText": "from jinja2.nodes import DerivedContextReference\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.nodes import DerivedContextReference\n", "kind": 7, "label": "DerivedContextReference", "sortText": "4a.9999.DerivedContextReference"}, {"additionalTextEdits": [{"newText": "from ast import Dict\n", "range": {"end": {"character": 0, "line": 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DictLoader\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.loaders import DictLoader\n", "kind": 7, "label": "DictLoader", "sortText": "4a.9999.DictLoader"}, {"additionalTextEdits": [{"newText": "from types import DynamicClassAttribute\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from types import DynamicClassAttribute\n", "kind": 7, "label": "DynamicClassAttribute", "sortText": "4a.9999.DynamicClassAttribute"}, {"additionalTextEdits": [{"newText": "from pickle import EMPTY_DICT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from pickle import EMPTY_DICT\n", "kind": 6, "label": "EMPTY_DICT", "sortText": "4a.9999.EMPTY_DICT"}, {"additionalTextEdits": [{"newText": "from stat import FILE_ATTRIBUTE_DIRECTORY\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from stat import FILE_ATTRIBUTE_DIRECTORY\n", "kind": 6, "label": "FILE_ATTRIBUTE_DIRECTORY", "sortText": "4a.9999.FILE_ATTRIBUTE_DIRECTORY"}, {"additionalTextEdits": [{"newText": "from click.types import FileInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import FileInfoDict\n", "kind": 7, "label": "FileInfoDict", "sortText": "4a.9999.FileInfoDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import FileMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import FileMultiDict\n", "kind": 7, "label": "FileMultiDict", "sortText": "4a.9999.FileMultiDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.file_storage import FileMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.file_storage import FileMultiDict\n", "kind": 7, "label": "FileMultiDict", "sortText": "4a.9999.FileMultiDict"}, {"additionalTextEdits": [{"newText": "from flask.debughelpers import FormDataRoutingRedirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.debughelpers import FormDataRoutingRedirect\n", "kind": 7, "label": "FormDataRoutingRedirect", "sortText": "4a.9999.FormDataRoutingRedirect"}, {"additionalTextEdits": [{"newText": "from click.types import FuncParamTypeInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import FuncParamTypeInfoDict\n", "kind": 7, "label": "FuncParamTypeInfoDict", "sortText": "4a.9999.FuncParamTypeInfoDict"}, {"additionalTextEdits": [{"newText": "from urllib3 import HTTPHeaderDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3 import HTTPHeaderDict\n", "kind": 7, "label": "HTTPHeaderDict", "sortText": "4a.9999.HTTPHeaderDict"}, {"additionalTextEdits": [{"newText": "from socket import IP_DEFAULT_MULTICAST_LOOP\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from socket import IP_DEFAULT_MULTICAST_LOOP\n", "kind": 6, "label": "IP_DEFAULT_MULTICAST_LOOP", "sortText": "4a.9999.IP_DEFAULT_MULTICAST_LOOP"}, {"additionalTextEdits": [{"newText": "from socket import IP_DEFAULT_MULTICAST_TTL\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from socket import IP_DEFAULT_MULTICAST_TTL\n", "kind": 6, "label": "IP_DEFAULT_MULTICAST_TTL", "sortText": "4a.9999.IP_DEFAULT_MULTICAST_TTL"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import ImmutableDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import ImmutableDict\n", "kind": 7, "label": "ImmutableDict", "sortText": "4a.9999.ImmutableDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import ImmutableDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import ImmutableDict\n", "kind": 7, "label": "ImmutableDict", "sortText": "4a.9999.ImmutableDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import ImmutableDictMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import ImmutableDictMixin\n", "kind": 7, "label": "ImmutableDictMixin", "sortText": "4a.9999.ImmutableDictMixin"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.mixins import ImmutableDictMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.mixins import ImmutableDictMixin\n", "kind": 7, "label": "ImmutableDictMixin", "sortText": "4a.9999.ImmutableDictMixin"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import ImmutableMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import ImmutableMultiDict\n", "kind": 7, "label": "ImmutableMultiDict", "sortText": "4a.9999.ImmutableMultiDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import ImmutableMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import ImmutableMultiDict\n", "kind": 7, "label": "ImmutableMultiDict", "sortText": "4a.9999.ImmutableMultiDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import ImmutableMultiDictMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import ImmutableMultiDictMixin\n", "kind": 7, "label": "ImmutableMultiDictMixin", "sortText": "4a.9999.ImmutableMultiDictMixin"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.mixins import ImmutableMultiDictMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.mixins import ImmutableMultiDictMixin\n", "kind": 7, "label": "ImmutableMultiDictMixin", "sortText": "4a.9999.ImmutableMultiDictMixin"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import ImmutableTypeConversionDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import ImmutableTypeConversionDict\n", "kind": 7, "label": "ImmutableTypeConversionDict", "sortText": "4a.9999.ImmutableTypeConversionDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import ImmutableTypeConversionDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import ImmutableTypeConversionDict\n", "kind": 7, "label": "ImmutableTypeConversionDict", "sortText": "4a.9999.ImmutableTypeConversionDict"}, {"additionalTextEdits": [{"newText": "from idna import InvalidCodepointContext\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from idna import InvalidCodepointContext\n", "kind": 7, "label": "InvalidCodepointContext", "sortText": "4a.9999.InvalidCodepointContext"}, {"additionalTextEdits": [{"newText": "from idna.core import InvalidCodepointContext\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from idna.core import InvalidCodepointContext\n", "kind": 7, "label": "InvalidCodepointContext", "sortText": "4a.9999.InvalidCodepointContext"}, {"additionalTextEdits": [{"newText": "from requests.structures import LookupDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.structures import LookupDict\n", "kind": 7, "label": "LookupDict", "sortText": "4a.9999.LookupDict"}, {"additionalTextEdits": [{"newText": "from http.client import MISDIRECTED_REQUEST\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.client import MISDIRECTED_REQUEST\n", "kind": 6, "label": "MISDIRECTED_REQUEST", "sortText": "4a.9999.MISDIRECTED_REQUEST"}, {"additionalTextEdits": [{"newText": "from werkzeug.exceptions import MisdirectedRequest\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.exceptions import MisdirectedRequest\n", "kind": 7, "label": "MisdirectedRequest", "sortText": "4a.9999.MisdirectedRequest"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import MultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import MultiDict\n", "kind": 7, "label": "MultiDict", "sortText": "4a.9999.MultiDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import MultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import MultiDict\n", "kind": 7, "label": "MultiDict", "sortText": "4a.9999.MultiDict"}, {"additionalTextEdits": [{"newText": "from click.types import NumberRangeInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import NumberRangeInfoDict\n", "kind": 7, "label": "NumberRangeInfoDict", "sortText": "4a.9999.NumberRangeInfoDict"}, {"additionalTextEdits": [{"newText": "from os import O_DIRECT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from os import O_DIRECT\n", "kind": 6, "label": "O_DIRECT", "sortText": "4a.9999.O_DIRECT"}, {"additionalTextEdits": [{"newText": "from os import O_DIRECTORY\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from os import O_DIRECTORY\n", "kind": 6, "label": "O_DIRECTORY", "sortText": "4a.9999.O_DIRECTORY"}, {"additionalTextEdits": [{"newText": "from collections import OrderedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import OrderedDict\n", "kind": 7, "label": "OrderedDict", "sortText": "4a.9999.OrderedDict"}, {"additionalTextEdits": [{"newText": "from requests.sessions import OrderedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import OrderedDict\n", "kind": 6, "label": "OrderedDict", "sortText": "4a.9999.OrderedDict"}, {"additionalTextEdits": [{"newText": "from requests.utils import OrderedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import OrderedDict\n", "kind": 6, "label": "OrderedDict", "sortText": "4a.9999.OrderedDict"}, {"additionalTextEdits": [{"newText": "from typing_extensions import OrderedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing_extensions import OrderedDict\n", "kind": 6, "label": "OrderedDict", "sortText": "4a.9999.OrderedDict"}, {"additionalTextEdits": [{"newText": "from http.client import PERMANENT_REDIRECT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.client import PERMANENT_REDIRECT\n", "kind": 6, "label": "PERMANENT_REDIRECT", "sortText": "4a.9999.PERMANENT_REDIRECT"}, {"additionalTextEdits": [{"newText": "from click.types import ParamTypeInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import ParamTypeInfoDict\n", "kind": 7, "label": "ParamTypeInfoDict", "sortText": "4a.9999.ParamTypeInfoDict"}, {"additionalTextEdits": [{"newText": "from flask.json.tag import PassDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.json.tag import PassDict\n", "kind": 7, "label": "PassDict", "sortText": "4a.9999.PassDict"}, {"additionalTextEdits": [{"newText": "from click.types import PathInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import PathInfoDict\n", "kind": 7, "label": "PathInfoDict", "sortText": "4a.9999.PathInfoDict"}, {"additionalTextEdits": [{"newText": "from requests.models import REDIRECT_STATI\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.models import REDIRECT_STATI\n", "kind": 6, "label": "REDIRECT_STATI", "sortText": "4a.9999.REDIRECT_STATI"}, {"additionalTextEdits": [{"newText": "from requests.sessions import REDIRECT_STATI\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import REDIRECT_STATI\n", "kind": 6, "label": "REDIRECT_STATI", "sortText": "4a.9999.REDIRECT_STATI"}, {"additionalTextEdits": [{"newText": "from uuid import RESERVED_MICROSOFT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from uuid import RESERVED_MICROSOFT\n", "kind": 6, "label": "RESERVED_MICROSOFT", "sortText": "4a.9999.RESERVED_MICROSOFT"}, {"additionalTextEdits": [{"newText": "from http.client import RemoteDisconnected\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.client import RemoteDisconnected\n", "kind": 7, "label": "RemoteDisconnected", "sortText": "4a.9999.RemoteDisconnected"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing import RequestAliasRedirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing import RequestAliasRedirect\n", "kind": 7, "label": "RequestAliasRedirect", "sortText": "4a.9999.RequestAliasRedirect"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.exceptions import RequestAliasRedirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.exceptions import RequestAliasRedirect\n", "kind": 7, "label": "RequestAliasRedirect", "sortText": "4a.9999.RequestAliasRedirect"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing import RequestRedirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing import RequestRedirect\n", "kind": 7, "label": "RequestRedirect", "sortText": "4a.9999.RequestRedirect"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.exceptions import RequestRedirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.exceptions import RequestRedirect\n", "kind": 7, "label": "RequestRedirect", "sortText": "4a.9999.RequestRedirect"}, {"additionalTextEdits": [{"newText": "from requests.sessions import SessionRedirectMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import SessionRedirectMixin\n", "kind": 7, "label": "SessionRedirectMixin", "sortText": "4a.9999.SessionRedirectMixin"}, {"additionalTextEdits": [{"newText": "from http.client import TEMPORARY_REDIRECT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.client import TEMPORARY_REDIRECT\n", "kind": 6, "label": "TEMPORARY_REDIRECT", "sortText": "4a.9999.TEMPORARY_REDIRECT"}, {"additionalTextEdits": [{"newText": "from flask.json.tag import TagDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.json.tag import TagDict\n", "kind": 7, "label": "TagDict", "sortText": "4a.9999.TagDict"}, {"additionalTextEdits": [{"newText": "from tempfile import TemporaryDirectory\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from tempfile import TemporaryDirectory\n", "kind": 7, "label": "TemporaryDirectory", "sortText": "4a.9999.TemporaryDirectory"}, {"additionalTextEdits": [{"newText": "from requests import TooManyRedirects\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests import TooManyRedirects\n", "kind": 7, "label": "TooManyRedirects", "sortText": "4a.9999.TooManyRedirects"}, {"additionalTextEdits": [{"newText": "from requests.exceptions import TooManyRedirects\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.exceptions import TooManyRedirects\n", "kind": 7, "label": "TooManyRedirects", "sortText": "4a.9999.TooManyRedirects"}, {"additionalTextEdits": [{"newText": "from requests.sessions import TooManyRedirects\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import TooManyRedirects\n", "kind": 6, "label": "TooManyRedirects", "sortText": "4a.9999.TooManyRedirects"}, {"additionalTextEdits": [{"newText": "from click.types import TupleInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import TupleInfoDict\n", "kind": 7, "label": "TupleInfoDict", "sortText": "4a.9999.TupleInfoDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import TypeConversionDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import TypeConversionDict\n", "kind": 7, "label": "TypeConversionDict", "sortText": "4a.9999.TypeConversionDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import TypeConversionDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import TypeConversionDict\n", "kind": 7, "label": "TypeConversionDict", "sortText": "4a.9999.TypeConversionDict"}, {"additionalTextEdits": [{"newText": "from typing import TypedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import TypedDict\n", "kind": 6, "label": "TypedDict", "sortText": "4a.9999.TypedDict"}, {"additionalTextEdits": [{"newText": "from typing_extensions import TypedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing_extensions import TypedDict\n", "kind": 6, "label": "TypedDict", "sortText": "4a.9999.TypedDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import UpdateDictMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import UpdateDictMixin\n", "kind": 7, "label": "UpdateDictMixin", "sortText": "4a.9999.UpdateDictMixin"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.mixins import UpdateDictMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.mixins import UpdateDictMixin\n", "kind": 7, "label": "UpdateDictMixin", "sortText": "4a.9999.UpdateDictMixin"}, {"additionalTextEdits": [{"newText": "from collections import UserDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import UserDict\n", "kind": 7, "label": "UserDict", "sortText": "4a.9999.UserDict"}, {"additionalTextEdits": [{"newText": "from weakref import WeakKeyDictionary\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from weakref import WeakKeyDictionary\n", "kind": 7, "label": "WeakKeyDictionary", "sortText": "4a.9999.WeakKeyDictionary"}, {"additionalTextEdits": [{"newText": "from weakref import WeakValueDictionary\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from weakref import WeakValueDictionary\n", "kind": 7, "label": "WeakValueDictionary", "sortText": "4a.9999.WeakValueDictionary"}, {"additionalTextEdits": [{"newText": "from codecs import _BufferedIncrementalDecoder\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from codecs import _BufferedIncrementalDecoder\n", "kind": 7, "label": "_BufferedIncrementalDecoder", "sortText": "4a.9999._BufferedIncrementalDecoder"}, {"additionalTextEdits": [{"newText": "from enum import _EnumDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from enum import _EnumDict\n", "kind": 7, "label": "_EnumDict", "sortText": "4a.9999._EnumDict"}, {"additionalTextEdits": [{"newText": "from inspect import _GetMembersPredicate\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from inspect import _GetMembersPredicate\n", "kind": 6, "label": "_GetMembersPredicate", "sortText": "4a.9999._GetMembersPredicate"}, {"additionalTextEdits": [{"newText": "from inspect import _GetMembersPredicateTypeGuard\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from inspect import _GetMembersPredicateTypeGuard\n", "kind": 6, "label": "_GetMembersPredicateTypeGuard", "sortText": "4a.9999._GetMembersPredicateTypeGuard"}, {"additionalTextEdits": [{"newText": "from inspect import _GetMembersPredicateTypeIs\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from inspect import _GetMembersPredicateTypeIs\n", "kind": 6, "label": "_GetMembersPredicateTypeIs", "sortText": "4a.9999._GetMembersPredicateTypeIs"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import _ImmutableOrderedMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import _ImmutableOrderedMultiDict\n", "kind": 7, "label": "_ImmutableOrderedMultiDict", "sortText": "4a.9999._ImmutableOrderedMultiDict"}, {"additionalTextEdits": [{"newText": "from collections import _OrderedDictItemsView\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import _OrderedDictItemsView\n", "kind": 7, "label": "_OrderedDictItemsView", "sortText": "4a.9999._OrderedDictItemsView"}, {"additionalTextEdits": [{"newText": "from collections import _OrderedDictKeysView\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import _OrderedDictKeysView\n", "kind": 7, "label": "_OrderedDictKeysView", "sortText": "4a.9999._OrderedDictKeysView"}, {"additionalTextEdits": [{"newText": "from collections import _OrderedDictValuesView\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import _OrderedDictValuesView\n", "kind": 7, "label": "_OrderedDictValuesView", "sortText": "4a.9999._OrderedDictValuesView"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import _OrderedMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import _OrderedMultiDict\n", "kind": 7, "label": "_OrderedMultiDict", "sortText": "4a.9999._OrderedMultiDict"}, {"additionalTextEdits": [{"newText": "from ssl import _PeerCertRetDictType\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import _PeerCertRetDictType\n", "kind": 6, "label": "_PeerCertRetDictType", "sortText": "4a.9999._PeerCertRetDictType"}, {"additionalTextEdits": [{"newText": "from itertools import _Predicate\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itertools import _Predicate\n", "kind": 6, "label": "_Predicate", "sortText": "4a.9999._Predicate"}, {"additionalTextEdits": [{"newText": "from contextlib import _RedirectStream\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextlib import _RedirectStream\n", "kind": 7, "label": "_RedirectStream", "sortText": "4a.9999._RedirectStream"}, {"additionalTextEdits": [{"newText": "from click.shell_completion import _SourceVarsDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.shell_completion import _SourceVarsDict\n", "kind": 7, "label": "_SourceVarsDict", "sortText": "4a.9999._SourceVarsDict"}, {"additionalTextEdits": [{"newText": "from contextlib import _SupportsRedirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextlib import _SupportsRedirect\n", "kind": 7, "label": "_SupportsRedirect", "sortText": "4a.9999._SupportsRedirect"}, {"additionalTextEdits": [{"newText": "from contextlib import _SupportsRedirectT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextlib import _SupportsRedirectT\n", "kind": 6, "label": "_SupportsRedirectT", "sortText": "4a.9999._SupportsRedirectT"}, {"additionalTextEdits": [{"newText": "from werkzeug.test import _TAnyMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.test import _TAnyMultiDict\n", "kind": 6, "label": "_TAnyMultiDict", "sortText": "4a.9999._TAnyMultiDict"}, {"additionalTextEdits": [{"newText": "from urllib3.util.ssl_ import _TYPE_PEER_CERT_RET_DICT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.ssl_ import _TYPE_PEER_CERT_RET_DICT\n", "kind": 7, "label": "_TYPE_PEER_CERT_RET_DICT", "sortText": "4a.9999._TYPE_PEER_CERT_RET_DICT"}, {"additionalTextEdits": [{"newText": "from typing import _TypedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import _TypedDict\n", "kind": 7, "label": "_TypedDict", "sortText": "4a.9999._TypedDict"}, {"additionalTextEdits": [{"newText": "from typing_extensions import _TypedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing_extensions import _TypedDict\n", "kind": 7, "label": "_TypedDict", "sortText": "4a.9999._TypedDict"}, {"additionalTextEdits": [{"newText": "from colorama.ansitowin32 import _WinTermCallDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from colorama.ansitowin32 import _WinTermCallDict\n", "kind": 6, "label": "_WinTermCallDict", "sortText": "4a.9999._WinTermCallDict"}, {"additionalTextEdits": [{"newText": "from urllib3.connection import _WrappedAndVerifiedSocket\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.connection import _WrappedAndVerifiedSocket\n", "kind": 7, "label": "_WrappedAndVerifiedSocket", "sortText": "4a.9999._WrappedAndVerifiedSocket"}, {"additionalTextEdits": [{"newText": "from abc import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from abc import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from ast import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ast import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.base_events import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.base_events import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.coroutines import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.coroutines import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.events import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.events import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.futures import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.futures import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.locks import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.locks import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.protocols import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.protocols import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.queues import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.queues import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.runners import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.runners import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.streams import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.streams import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.subprocess import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.subprocess import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.taskgroups import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.taskgroups import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.tasks import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.tasks import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.threads import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.threads import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.timeouts import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.timeouts import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.transports import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.transports import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.unix_events import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.unix_events import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from atexit import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from atexit import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from base64 import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from base64 import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from binascii import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from binascii import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from blinker import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from blinker import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from blinker.base import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from blinker.base import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.core import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.core import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.decorators import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.decorators import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.formatting import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.formatting import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.globals import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.globals import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.parser import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.parser import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.shell_completion import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.shell_completion import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.termui import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.termui import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.testing import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.testing import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.types import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from code import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from code import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from codecs import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from codecs import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from collections import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from collections.abc import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections.abc import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from colorama import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from colorama import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from colorama.ansitowin32 import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from colorama.ansitowin32 import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from contextlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from contextvars import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextvars import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from copy import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from copy import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from dataclasses import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from dataclasses import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from datetime import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from datetime import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from decimal import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from decimal import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from difflib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from difflib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from email.utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from email.utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from enum import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from enum import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from errno import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from errno import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.app import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.app import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.blueprints import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.blueprints import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.cli import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.cli import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.config import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.config import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.ctx import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.ctx import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.debughelpers import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.debughelpers import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.globals import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.globals import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.helpers import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.helpers import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.json import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.json import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.json.provider import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.json.provider import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.json.tag import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.json.tag import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.logging import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.logging import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.sansio.app import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.sansio.app import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.sansio.blueprints import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.sansio.blueprints import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.sansio.scaffold import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.sansio.scaffold import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.sessions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.sessions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.signals import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.signals import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.templating import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.templating import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.testing import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.testing import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.typing import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.typing import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.wrappers import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.wrappers import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from fnmatch import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from fnmatch import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from functools import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from functools import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from genericpath import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from genericpath import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from getpass import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from getpass import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from gettext import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from gettext import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from glob import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from glob import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from hashlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from hashlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from hmac import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from hmac import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from html import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from html import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from http import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from http.client import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.client import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from http.cookiejar import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.cookiejar import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from http.cookies import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.cookies import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from http.server import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.server import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from idna import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from idna import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from idna.core import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from idna.core import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib.abc import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib.abc import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib.machinery import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib.machinery import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib.metadata import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib.metadata import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib.util import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib.util import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from inspect import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from inspect import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from io import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from io import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itertools import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itertools import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous.encoding import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous.encoding import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous.exc import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous.exc import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous.serializer import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous.serializer import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous.signer import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous.signer import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous.timed import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous.timed import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous.url_safe import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous.url_safe import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2 import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2 import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.async_utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.async_utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.bccache import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.bccache import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.compiler import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.compiler import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.debug import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.debug import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.environment import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.environment import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.lexer import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.lexer import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.loaders import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.loaders import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.nodes import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.nodes import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.parser import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.parser import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.runtime import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.runtime import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from json import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from json import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from json.decoder import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from json.decoder import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from json.encoder import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from json.encoder import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from logging import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from logging import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from markupsafe import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from markupsafe import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from marshal import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from marshal import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from math import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from math import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from mimetypes import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from mimetypes import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from numbers import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from numbers import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from operator import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from operator import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from os import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from os import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from os.path import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from os.path import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from pathlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from pathlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from pickle import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from pickle import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from pkgutil import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from pkgutil import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from platform import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from platform import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from posixpath import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from posixpath import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from pprint import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from pprint import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from queue import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from queue import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from random import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from random import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from re import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from re import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from readline import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from readline import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.adapters import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.adapters import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.api import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.api import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.auth import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.auth import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.compat import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.compat import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.cookies import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.cookies import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.hooks import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.hooks import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.models import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.models import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.packages import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.packages import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.sessions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.status_codes import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.status_codes import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.structures import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.structures import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from rlcompleter import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from rlcompleter import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from secrets import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from secrets import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from selectors import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from selectors import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from shlex import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from shlex import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from shutil import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from shutil import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from socket import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from socket import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from socketserver import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from socketserver import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from socks import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from socks import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from ssl import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from stat import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from stat import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from string import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from string import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from subprocess import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from subprocess import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from sys import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from sys import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from tempfile import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from tempfile import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from termios import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from termios import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from threading import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from threading import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from time import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from time import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from traceback import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from traceback import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from tty import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from tty import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from types import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from types import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from typing import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from typing_extensions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing_extensions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from unicodedata import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from unicodedata import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib.parse import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib.parse import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3 import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3 import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.connection import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.connection import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.connectionpool import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.connectionpool import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.contrib.socks import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.contrib.socks import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.fields import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.fields import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.filepost import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.filepost import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.poolmanager import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.poolmanager import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.response import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.response import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.connection import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.connection import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.proxy import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.proxy import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.request import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.request import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.response import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.response import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.retry import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.retry import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.ssl_ import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.ssl_ import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.timeout import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.timeout import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.url import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.url import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.util import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.util import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from uuid import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from uuid import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from warnings import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from warnings import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from weakref import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from weakref import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from webbrowser import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from webbrowser import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.accept import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.accept import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.auth import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.auth import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.cache_control import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.cache_control import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.csp import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.csp import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.etag import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.etag import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.file_storage import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.file_storage import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.headers import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.headers import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.mixins import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.mixins import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.range import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.range import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.debug import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.debug import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.debug.tbtools import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.debug.tbtools import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.formparser import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.formparser import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.http import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.http import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.local import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.local import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.middleware.shared_data import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.middleware.shared_data import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.converters import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.converters import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.map import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.map import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.matcher import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.matcher import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.rules import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.rules import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.sansio import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.sansio import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.sansio.http import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.sansio.http import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.sansio.multipart import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.sansio.multipart import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.sansio.request import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.sansio.request import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.sansio.response import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.sansio.response import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.sansio.utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.sansio.utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.security import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.security import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.serving import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.serving import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.test import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.test import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.urls import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.urls import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.user_agent import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.user_agent import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.wrappers import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.wrappers import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.wrappers.request import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.wrappers.request import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.wrappers.response import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.wrappers.response import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.wsgi import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.wsgi import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from wsgiref.types import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from wsgiref.types import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from zipfile import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from zipfile import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from zipimport import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from zipimport import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from zlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from zlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from idna.core import _bidi_rtl_categories\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from idna.core import _bidi_rtl_categories\n", "kind": 6, "label": "_bidi_rtl_categories", "sortText": "4a.9999._bidi_rtl_categories"}, {"additionalTextEdits": [{"newText": "from jinja2.runtime import _dict_method_all\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.runtime import _dict_method_all\n", "kind": 3, "label": "_dict_method_all", "sortText": "4a.9999._dict_method_all"}, {"additionalTextEdits": [{"newText": "from importlib.util import _incompatible_extension_module_restrictions\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib.util import _incompatible_extension_module_restrictions\n", "kind": 7, "label": "_incompatible_extension_module_restrictions", "sortText": "4a.9999._incompatible_extension_module_restrictions"}, {"additionalTextEdits": [{"newText": "from collections import _odict_items\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import _odict_items\n", "kind": 7, "label": "_odict_items", "sortText": "4a.9999._odict_items"}, {"additionalTextEdits": [{"newText": "from collections import _odict_keys\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import _odict_keys\n", "kind": 7, "label": "_odict_keys", "sortText": "4a.9999._odict_keys"}, {"additionalTextEdits": [{"newText": "from collections import _odict_values\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import _odict_values\n", "kind": 7, "label": "_odict_values", "sortText": "4a.9999._odict_values"}, {"additionalTextEdits": [{"newText": "from flask.helpers import _wz_redirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.helpers import _wz_redirect\n", "kind": 3, "label": "_wz_redirect", "sortText": "4a.9999._wz_redirect"}, {"additionalTextEdits": [{"newText": "from flask.sansio.app import _wz_redirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.sansio.app import _wz_redirect\n", "kind": 3, "label": "_wz_redirect", "sortText": "4a.9999._wz_redirect"}, {"additionalTextEdits": [{"newText": "from requests.utils import add_dict_to_cookiejar\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import add_dict_to_cookiejar\n", "kind": 3, "label": "add_dict_to_cookiejar", "sortText": "4a.9999.add_dict_to_cookiejar"}, {"additionalTextEdits": [{"newText": "from werkzeug.utils import append_slash_redirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.utils import append_slash_redirect\n", "kind": 3, "label": "append_slash_redirect", "sortText": "4a.9999.append_slash_redirect"}, {"additionalTextEdits": [{"newText": "from dataclasses import asdict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from dataclasses import asdict\n", "kind": 3, "label": "asdict", "sortText": "4a.9999.asdict"}, {"additionalTextEdits": [{"newText": "from flask.debughelpers import attach_enctype_error_multidict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.debughelpers import attach_enctype_error_multidict\n", "kind": 3, "label": "attach_enctype_error_multidict", "sortText": "4a.9999.attach_enctype_error_multidict"}, {"additionalTextEdits": [{"newText": "from unicodedata import bidirectional\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from unicodedata import bidirectional\n", "kind": 3, "label": "bidirectional", "sortText": "4a.9999.bidirectional"}, {"additionalTextEdits": [{"newText": "from requests.cookies import cookiejar_from_dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.cookies import cookiejar_from_dict\n", "kind": 3, "label": "cookiejar_from_dict", "sortText": "4a.9999.cookiejar_from_dict"}, {"additionalTextEdits": [{"newText": "from requests.models import cookiejar_from_dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.models import cookiejar_from_dict\n", "kind": 6, "label": "cookiejar_from_dict", "sortText": "4a.9999.cookiejar_from_dict"}, {"additionalTextEdits": [{"newText": "from requests.sessions import cookiejar_from_dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import cookiejar_from_dict\n", "kind": 6, "label": "cookiejar_from_dict", "sortText": "4a.9999.cookiejar_from_dict"}, {"additionalTextEdits": [{"newText": "from requests.utils import cookiejar_from_dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import cookiejar_from_dict\n", "kind": 6, "label": "cookiejar_from_dict", "sortText": "4a.9999.cookiejar_from_dict"}, {"additionalTextEdits": [{"newText": "from sys import deactivate_stack_trampoline\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from sys import deactivate_stack_trampoline\n", "kind": 3, "label": "deactivate_stack_trampoline", "sortText": "4a.9999.deactivate_stack_trampoline"}, {"additionalTextEdits": [{"newText": "from collections import defaultdict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import defaultdict\n", "kind": 7, "label": "defaultdict", "sortText": "4a.9999.defaultdict"}, {"additionalTextEdits": [{"newText": "from requests.utils import dict_from_cookiejar\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import dict_from_cookiejar\n", "kind": 3, "label": "dict_from_cookiejar", "sortText": "4a.9999.dict_from_cookiejar"}, {"additionalTextEdits": [{"newText": "from requests.utils import dict_to_sequence\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import dict_to_sequence\n", "kind": 3, "label": "dict_to_sequence", "sortText": "4a.9999.dict_to_sequence"}, {"additionalTextEdits": [{"newText": "from requests.utils import get_encodings_from_content\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import get_encodings_from_content\n", "kind": 3, "label": "get_encodings_from_content", "sortText": "4a.9999.get_encodings_from_content"}, {"additionalTextEdits": [{"newText": "from typing import is_typeddict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import is_typeddict\n", "kind": 3, "label": "is_typeddict", "sortText": "4a.9999.is_typeddict"}, {"additionalTextEdits": [{"newText": "from requests.auth import parse_dict_header\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.auth import parse_dict_header\n", "kind": 6, "label": "parse_dict_header", "sortText": "4a.9999.parse_dict_header"}, {"additionalTextEdits": [{"newText": "from requests.utils import parse_dict_header\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import parse_dict_header\n", "kind": 3, "label": "parse_dict_header", "sortText": "4a.9999.parse_dict_header"}, {"additionalTextEdits": [{"newText": "from werkzeug.http import parse_dict_header\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.http import parse_dict_header\n", "kind": 3, "label": "parse_dict_header", "sortText": "4a.9999.parse_dict_header"}, {"additionalTextEdits": [{"newText": "from flask import redirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask import redirect\n", "kind": 3, "label": "redirect", "sortText": "4a.9999.redirect"}, {"additionalTextEdits": [{"newText": "from flask.helpers import redirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.helpers import redirect\n", "kind": 3, "label": "redirect", "sortText": "4a.9999.redirect"}, {"additionalTextEdits": [{"newText": "from werkzeug.utils import redirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.utils import redirect\n", "kind": 3, "label": "redirect", "sortText": "4a.9999.redirect"}, {"additionalTextEdits": [{"newText": "from contextlib import redirect_stderr\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextlib import redirect_stderr\n", "kind": 7, "label": "redirect_stderr", "sortText": "4a.9999.redirect_stderr"}, {"additionalTextEdits": [{"newText": "from contextlib import redirect_stdout\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextlib import redirect_stdout\n", "kind": 7, "label": "redirect_stdout", "sortText": "4a.9999.redirect_stdout"}, {"additionalTextEdits": [{"newText": "from flask import send_from_directory\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask import send_from_directory\n", "kind": 3, "label": "send_from_directory", "sortText": "4a.9999.send_from_directory"}, {"additionalTextEdits": [{"newText": "from flask.helpers import send_from_directory\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.helpers import send_from_directory\n", "kind": 3, "label": "send_from_directory", "sortText": "4a.9999.send_from_directory"}, {"additionalTextEdits": [{"newText": "from werkzeug.utils import send_from_directory\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.utils import send_from_directory\n", "kind": 3, "label": "send_from_directory", "sortText": "4a.9999.send_from_directory"}, {"additionalTextEdits": [{"newText": "from functools import singledispatchmethod\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from functools import singledispatchmethod\n", "kind": 7, "label": "singledispatchmethod", "sortText": "4a.9999.singledispatchmethod"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE\n", "kind": 6, "label": "ALERT_DESCRIPTION_BAD_CERTIFICATE", "sortText": "4b.9999.ALERT_DESCRIPTION_BAD_CERTIFICATE"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE\n", "kind": 6, "label": "ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE", "sortText": "4b.9999.ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE\n", "kind": 6, "label": "ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE", "sortText": "4b.9999.ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_EXPIRED\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_EXPIRED\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_EXPIRED", "sortText": "4b.9999.ALERT_DESCRIPTION_CERTIFICATE_EXPIRED"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_REVOKED\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_REVOKED\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_REVOKED", "sortText": "4b.9999.ALERT_DESCRIPTION_CERTIFICATE_REVOKED"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN", "sortText": "4b.9999.ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE", "sortText": "4b.9999.ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_CLOSE_NOTIFY\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_CLOSE_NOTIFY\n", "kind": 6, "label": "ALERT_DESCRIPTION_CLOSE_NOTIFY", "sortText": "4b.9999.ALERT_DESCRIPTION_CLOSE_NOTIFY"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_DECRYPT_ERROR\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_DECRYPT_ERROR\n", "kind": 6, "label": "ALERT_DESCRIPTION_DECRYPT_ERROR", "sortText": "4b.9999.ALERT_DESCRIPTION_DECRYPT_ERROR"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_INSUFFICIENT_SECURITY\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_INSUFFICIENT_SECURITY\n", "kind": 6, "label": "ALERT_DESCRIPTION_INSUFFICIENT_SECURITY", "sortText": "4b.9999.ALERT_DESCRIPTION_INSUFFICIENT_SECURITY"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_UNEXPECTED_MESSAGE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_UNEXPECTED_MESSAGE\n", "kind": 6, "label": "ALERT_DESCRIPTION_UNEXPECTED_MESSAGE", "sortText": "4b.9999.ALERT_DESCRIPTION_UNEXPECTED_MESSAGE"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE\n", "kind": 6, "label": "ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE", "sortText": "4b.9999.ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE"}, {"additionalTextEdits": [{"newText": "from urllib3._collections import HTTPHeaderDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._collections import HTTPHeaderDict\n", "kind": 7, "label": "HTTPHeaderDict", "sortText": "4b.9999.HTTPHeaderDict"}, {"additionalTextEdits": [{"newText": "from urllib3._collections import HTTPHeaderDictItemView\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._collections import HTTPHeaderDictItemView\n", "kind": 7, "label": "HTTPHeaderDictItemView", "sortText": "4b.9999.HTTPHeaderDictItemView"}, {"additionalTextEdits": [{"newText": "from _socket import IP_DEFAULT_MULTICAST_LOOP\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _socket import IP_DEFAULT_MULTICAST_LOOP\n", "kind": 6, "label": "IP_DEFAULT_MULTICAST_LOOP", "sortText": "4b.9999.IP_DEFAULT_MULTICAST_LOOP"}, {"additionalTextEdits": [{"newText": "from _socket import IP_DEFAULT_MULTICAST_TTL\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _socket import IP_DEFAULT_MULTICAST_TTL\n", "kind": 6, "label": "IP_DEFAULT_MULTICAST_TTL", "sortText": "4b.9999.IP_DEFAULT_MULTICAST_TTL"}, {"additionalTextEdits": [{"newText": "from _typeshed import IdentityFunction\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _typeshed import IdentityFunction\n", "kind": 7, "label": "IdentityFunction", "sortText": "4b.9999.IdentityFunction"}, {"additionalTextEdits": [{"newText": "from _typeshed._type_checker_internals import TypedDictFallback\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _typeshed._type_checker_internals import TypedDictFallback\n", "kind": 7, "label": "TypedDictFallback", "sortText": "4b.9999.TypedDictFallback"}, {"additionalTextEdits": [{"newText": "from werkzeug._internal import _DictAccessorProperty\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug._internal import _DictAccessorProperty\n", "kind": 7, "label": "_DictAccessorProperty", "sortText": "4b.9999._DictAccessorProperty"}, {"additionalTextEdits": [{"newText": "from _ssl import _PeerCertRetDictType\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import _PeerCertRetDictType\n", "kind": 6, "label": "_PeerCertRetDictType", "sortText": "4b.9999._PeerCertRetDictType"}, {"additionalTextEdits": [{"newText": "from __future__ import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from __future__ import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _codecs import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _codecs import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _collections_abc import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _collections_abc import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _contextvars import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _contextvars import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _frozen_importlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _frozen_importlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _frozen_importlib_external import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _frozen_importlib_external import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _hashlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _hashlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _io import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _io import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _operator import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _operator import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _pickle import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _pickle import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _random import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _random import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _socket import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _socket import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _ssl import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _stat import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _stat import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _thread import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _thread import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _typeshed import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _typeshed import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _typeshed._type_checker_internals import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _typeshed._type_checker_internals import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _typeshed.wsgi import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _typeshed.wsgi import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _warnings import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _warnings import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _weakref import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _weakref import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from blinker._utilities import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from blinker._utilities import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click._compat import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click._compat import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click._termui_impl import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click._termui_impl import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click._textwrap import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click._textwrap import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click._utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click._utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib._abc import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib._abc import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib._bootstrap import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib._bootstrap import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib._bootstrap_external import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib._bootstrap_external import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib.metadata._meta import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib.metadata._meta import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from markupsafe._native import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from markupsafe._native import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from markupsafe._speedups import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from markupsafe._speedups import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.__version__ import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.__version__ import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3._base_connection import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._base_connection import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3._collections import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._collections import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3._request_methods import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._request_methods import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3._version import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._version import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug._internal import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug._internal import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug._reloader import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug._reloader import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _collections_abc import dict_items\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _collections_abc import dict_items\n", "kind": 7, "label": "dict_items", "sortText": "4b.9999.dict_items"}, {"additionalTextEdits": [{"newText": "from _collections_abc import dict_keys\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _collections_abc import dict_keys\n", "kind": 7, "label": "dict_keys", "sortText": "4b.9999.dict_keys"}, {"additionalTextEdits": [{"newText": "from _collections_abc import dict_values\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _collections_abc import dict_values\n", "kind": 7, "label": "dict_values", "sortText": "4b.9999.dict_values"}, {"additionalTextEdits": [{"newText": "from urllib3._collections import ensure_can_construct_http_header_dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._collections import ensure_can_construct_http_header_dict\n", "kind": 3, "label": "ensure_can_construct_http_header_dict", "sortText": "4b.9999.ensure_can_construct_http_header_dict"}, {"additionalTextEdits": [{"newText": "from typing import DefaultDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import DefaultDict\n", "kind": 6, "label": "DefaultDict", "sortText": "9.9999.DefaultDict", "tags": [1]}, {"additionalTextEdits": [{"newText": "from typing import Dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import Dict\n", "kind": 6, "label": "Dict", "sortText": "9.9999.Dict", "tags": [1]}, {"additionalTextEdits": [{"newText": "from typing import OrderedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import OrderedDict\n", "kind": 6, "label": "OrderedDict", "sortText": "9.9999.OrderedDict", "tags": [1]}]}} {"suite": "web", "label": "request args completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 9, "character": 18, "iteration": 4, "result": {"isIncomplete": false, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"data": "builtin", "kind": 7, "label": "IsADirectoryError", "sortText": "0.9999.IsADirectoryError"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"data": "builtin", "kind": 7, "label": "NotADirectoryError", "sortText": "0.9999.NotADirectoryError"}, {"data": "builtin", "kind": 7, "label": "PendingDeprecationWarning", "sortText": "0.9999.PendingDeprecationWarning"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"data": "builtin", "kind": 7, "label": "dict", "sortText": "0.9999.dict"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}, {"data": "builtin", "kind": 21, "label": "__dict__", "sortText": "3.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE\n", "kind": 6, "label": "ALERT_DESCRIPTION_BAD_CERTIFICATE", "sortText": "4a.9999.ALERT_DESCRIPTION_BAD_CERTIFICATE"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE\n", "kind": 6, "label": "ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE", "sortText": "4a.9999.ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE\n", "kind": 6, "label": "ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE", "sortText": "4a.9999.ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_EXPIRED\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_EXPIRED\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_EXPIRED", "sortText": "4a.9999.ALERT_DESCRIPTION_CERTIFICATE_EXPIRED"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_REVOKED\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_REVOKED\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_REVOKED", "sortText": "4a.9999.ALERT_DESCRIPTION_CERTIFICATE_REVOKED"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN", "sortText": "4a.9999.ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE", "sortText": "4a.9999.ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_CLOSE_NOTIFY\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_CLOSE_NOTIFY\n", "kind": 6, "label": "ALERT_DESCRIPTION_CLOSE_NOTIFY", "sortText": "4a.9999.ALERT_DESCRIPTION_CLOSE_NOTIFY"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_DECRYPT_ERROR\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_DECRYPT_ERROR\n", "kind": 6, "label": "ALERT_DESCRIPTION_DECRYPT_ERROR", "sortText": "4a.9999.ALERT_DESCRIPTION_DECRYPT_ERROR"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_INSUFFICIENT_SECURITY\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_INSUFFICIENT_SECURITY\n", "kind": 6, "label": "ALERT_DESCRIPTION_INSUFFICIENT_SECURITY", "sortText": "4a.9999.ALERT_DESCRIPTION_INSUFFICIENT_SECURITY"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_UNEXPECTED_MESSAGE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_UNEXPECTED_MESSAGE\n", "kind": 6, "label": "ALERT_DESCRIPTION_UNEXPECTED_MESSAGE", "sortText": "4a.9999.ALERT_DESCRIPTION_UNEXPECTED_MESSAGE"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE\n", "kind": 6, "label": "ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE", "sortText": "4a.9999.ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE"}, {"additionalTextEdits": [{"newText": "from urllib3.exceptions import BodyNotHttplibCompatible\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.exceptions import BodyNotHttplibCompatible\n", "kind": 7, "label": "BodyNotHttplibCompatible", "sortText": "4a.9999.BodyNotHttplibCompatible"}, {"additionalTextEdits": [{"newText": "from codecs import BufferedIncrementalDecoder\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from codecs import BufferedIncrementalDecoder\n", "kind": 7, "label": "BufferedIncrementalDecoder", "sortText": "4a.9999.BufferedIncrementalDecoder"}, {"additionalTextEdits": [{"newText": "from codecs import BufferedIncrementalEncoder\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from codecs import BufferedIncrementalEncoder\n", "kind": 7, "label": "BufferedIncrementalEncoder", "sortText": "4a.9999.BufferedIncrementalEncoder"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import CallbackDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import CallbackDict\n", "kind": 7, "label": "CallbackDict", "sortText": "4a.9999.CallbackDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import CallbackDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import CallbackDict\n", "kind": 7, "label": "CallbackDict", "sortText": "4a.9999.CallbackDict"}, {"additionalTextEdits": [{"newText": "from requests.structures import CaseInsensitiveDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.structures import CaseInsensitiveDict\n", "kind": 7, "label": "CaseInsensitiveDict", "sortText": "4a.9999.CaseInsensitiveDict"}, {"additionalTextEdits": [{"newText": "from requests.utils import CaseInsensitiveDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import CaseInsensitiveDict\n", "kind": 6, "label": "CaseInsensitiveDict", "sortText": "4a.9999.CaseInsensitiveDict"}, {"additionalTextEdits": [{"newText": "from click.types import ChoiceInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import ChoiceInfoDict\n", "kind": 7, "label": "ChoiceInfoDict", "sortText": "4a.9999.ChoiceInfoDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.exceptions import ClientDisconnected\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.exceptions import ClientDisconnected\n", "kind": 7, "label": "ClientDisconnected", "sortText": "4a.9999.ClientDisconnected"}, {"additionalTextEdits": [{"newText": "from werkzeug.test import ClientRedirectError\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.test import ClientRedirectError\n", "kind": 7, "label": "ClientRedirectError", "sortText": "4a.9999.ClientRedirectError"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import CombinedMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import CombinedMultiDict\n", "kind": 7, "label": "CombinedMultiDict", "sortText": "4a.9999.CombinedMultiDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import CombinedMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import CombinedMultiDict\n", "kind": 7, "label": "CombinedMultiDict", "sortText": "4a.9999.CombinedMultiDict"}, {"additionalTextEdits": [{"newText": "from requests.models import DEFAULT_REDIRECT_LIMIT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.models import DEFAULT_REDIRECT_LIMIT\n", "kind": 6, "label": "DEFAULT_REDIRECT_LIMIT", "sortText": "4a.9999.DEFAULT_REDIRECT_LIMIT"}, {"additionalTextEdits": [{"newText": "from requests.sessions import DEFAULT_REDIRECT_LIMIT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import DEFAULT_REDIRECT_LIMIT\n", "kind": 6, "label": "DEFAULT_REDIRECT_LIMIT", "sortText": "4a.9999.DEFAULT_REDIRECT_LIMIT"}, {"additionalTextEdits": [{"newText": "from pickle import DICT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from pickle import DICT\n", "kind": 6, "label": "DICT", "sortText": "4a.9999.DICT"}, {"additionalTextEdits": [{"newText": "from click.types import DateTimeInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import DateTimeInfoDict\n", "kind": 7, "label": "DateTimeInfoDict", "sortText": "4a.9999.DateTimeInfoDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.debug import DebuggedApplication\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.debug import DebuggedApplication\n", "kind": 7, "label": "DebuggedApplication", "sortText": "4a.9999.DebuggedApplication"}, {"additionalTextEdits": [{"newText": "from decimal import DecimalException\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from decimal import DecimalException\n", "kind": 7, "label": "DecimalException", "sortText": "4a.9999.DecimalException"}, {"additionalTextEdits": [{"newText": "from jinja2.nodes import DerivedContextReference\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.nodes import DerivedContextReference\n", "kind": 7, "label": "DerivedContextReference", "sortText": "4a.9999.DerivedContextReference"}, {"additionalTextEdits": [{"newText": "from ast import Dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ast import Dict\n", "kind": 7, "label": "Dict", "sortText": "4a.9999.Dict"}, {"additionalTextEdits": [{"newText": "from jinja2.nodes import Dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.nodes import Dict\n", "kind": 7, "label": "Dict", "sortText": "4a.9999.Dict"}, {"additionalTextEdits": [{"newText": "from ast import DictComp\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ast import DictComp\n", "kind": 7, "label": "DictComp", "sortText": "4a.9999.DictComp"}, {"additionalTextEdits": [{"newText": "from jinja2 import DictLoader\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2 import DictLoader\n", "kind": 7, "label": "DictLoader", "sortText": "4a.9999.DictLoader"}, {"additionalTextEdits": [{"newText": "from jinja2.loaders import DictLoader\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.loaders import DictLoader\n", "kind": 7, "label": "DictLoader", "sortText": "4a.9999.DictLoader"}, {"additionalTextEdits": [{"newText": "from types import DynamicClassAttribute\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from types import DynamicClassAttribute\n", "kind": 7, "label": "DynamicClassAttribute", "sortText": "4a.9999.DynamicClassAttribute"}, {"additionalTextEdits": [{"newText": "from pickle import EMPTY_DICT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from pickle import EMPTY_DICT\n", "kind": 6, "label": "EMPTY_DICT", "sortText": "4a.9999.EMPTY_DICT"}, {"additionalTextEdits": [{"newText": "from stat import FILE_ATTRIBUTE_DIRECTORY\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from stat import FILE_ATTRIBUTE_DIRECTORY\n", "kind": 6, "label": "FILE_ATTRIBUTE_DIRECTORY", "sortText": "4a.9999.FILE_ATTRIBUTE_DIRECTORY"}, {"additionalTextEdits": [{"newText": "from click.types import FileInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import FileInfoDict\n", "kind": 7, "label": "FileInfoDict", "sortText": "4a.9999.FileInfoDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import FileMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import FileMultiDict\n", "kind": 7, "label": "FileMultiDict", "sortText": "4a.9999.FileMultiDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.file_storage import FileMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.file_storage import FileMultiDict\n", "kind": 7, "label": "FileMultiDict", "sortText": "4a.9999.FileMultiDict"}, {"additionalTextEdits": [{"newText": "from flask.debughelpers import FormDataRoutingRedirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.debughelpers import FormDataRoutingRedirect\n", "kind": 7, "label": "FormDataRoutingRedirect", "sortText": "4a.9999.FormDataRoutingRedirect"}, {"additionalTextEdits": [{"newText": "from click.types import FuncParamTypeInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import FuncParamTypeInfoDict\n", "kind": 7, "label": "FuncParamTypeInfoDict", "sortText": "4a.9999.FuncParamTypeInfoDict"}, {"additionalTextEdits": [{"newText": "from urllib3 import HTTPHeaderDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3 import HTTPHeaderDict\n", "kind": 7, "label": "HTTPHeaderDict", "sortText": "4a.9999.HTTPHeaderDict"}, {"additionalTextEdits": [{"newText": "from socket import IP_DEFAULT_MULTICAST_LOOP\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from socket import IP_DEFAULT_MULTICAST_LOOP\n", "kind": 6, "label": "IP_DEFAULT_MULTICAST_LOOP", "sortText": "4a.9999.IP_DEFAULT_MULTICAST_LOOP"}, {"additionalTextEdits": [{"newText": "from socket import IP_DEFAULT_MULTICAST_TTL\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from socket import IP_DEFAULT_MULTICAST_TTL\n", "kind": 6, "label": "IP_DEFAULT_MULTICAST_TTL", "sortText": "4a.9999.IP_DEFAULT_MULTICAST_TTL"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import ImmutableDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import ImmutableDict\n", "kind": 7, "label": "ImmutableDict", "sortText": "4a.9999.ImmutableDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import ImmutableDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import ImmutableDict\n", "kind": 7, "label": "ImmutableDict", "sortText": "4a.9999.ImmutableDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import ImmutableDictMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import ImmutableDictMixin\n", "kind": 7, "label": "ImmutableDictMixin", "sortText": "4a.9999.ImmutableDictMixin"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.mixins import ImmutableDictMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.mixins import ImmutableDictMixin\n", "kind": 7, "label": "ImmutableDictMixin", "sortText": "4a.9999.ImmutableDictMixin"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import ImmutableMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import ImmutableMultiDict\n", "kind": 7, "label": "ImmutableMultiDict", "sortText": "4a.9999.ImmutableMultiDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import ImmutableMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import ImmutableMultiDict\n", "kind": 7, "label": "ImmutableMultiDict", "sortText": "4a.9999.ImmutableMultiDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import ImmutableMultiDictMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import ImmutableMultiDictMixin\n", "kind": 7, "label": "ImmutableMultiDictMixin", "sortText": "4a.9999.ImmutableMultiDictMixin"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.mixins import ImmutableMultiDictMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.mixins import ImmutableMultiDictMixin\n", "kind": 7, "label": "ImmutableMultiDictMixin", "sortText": "4a.9999.ImmutableMultiDictMixin"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import ImmutableTypeConversionDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import ImmutableTypeConversionDict\n", "kind": 7, "label": "ImmutableTypeConversionDict", "sortText": "4a.9999.ImmutableTypeConversionDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import ImmutableTypeConversionDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import ImmutableTypeConversionDict\n", "kind": 7, "label": "ImmutableTypeConversionDict", "sortText": "4a.9999.ImmutableTypeConversionDict"}, {"additionalTextEdits": [{"newText": "from idna import InvalidCodepointContext\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from idna import InvalidCodepointContext\n", "kind": 7, "label": "InvalidCodepointContext", "sortText": "4a.9999.InvalidCodepointContext"}, {"additionalTextEdits": [{"newText": "from idna.core import InvalidCodepointContext\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from idna.core import InvalidCodepointContext\n", "kind": 7, "label": "InvalidCodepointContext", "sortText": "4a.9999.InvalidCodepointContext"}, {"additionalTextEdits": [{"newText": "from requests.structures import LookupDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.structures import LookupDict\n", "kind": 7, "label": "LookupDict", "sortText": "4a.9999.LookupDict"}, {"additionalTextEdits": [{"newText": "from http.client import MISDIRECTED_REQUEST\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.client import MISDIRECTED_REQUEST\n", "kind": 6, "label": "MISDIRECTED_REQUEST", "sortText": "4a.9999.MISDIRECTED_REQUEST"}, {"additionalTextEdits": [{"newText": "from werkzeug.exceptions import MisdirectedRequest\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.exceptions import MisdirectedRequest\n", "kind": 7, "label": "MisdirectedRequest", "sortText": "4a.9999.MisdirectedRequest"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import MultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import MultiDict\n", "kind": 7, "label": "MultiDict", "sortText": "4a.9999.MultiDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import MultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import MultiDict\n", "kind": 7, "label": "MultiDict", "sortText": "4a.9999.MultiDict"}, {"additionalTextEdits": [{"newText": "from click.types import NumberRangeInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import NumberRangeInfoDict\n", "kind": 7, "label": "NumberRangeInfoDict", "sortText": "4a.9999.NumberRangeInfoDict"}, {"additionalTextEdits": [{"newText": "from os import O_DIRECT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from os import O_DIRECT\n", "kind": 6, "label": "O_DIRECT", "sortText": "4a.9999.O_DIRECT"}, {"additionalTextEdits": [{"newText": "from os import O_DIRECTORY\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from os import O_DIRECTORY\n", "kind": 6, "label": "O_DIRECTORY", "sortText": "4a.9999.O_DIRECTORY"}, {"additionalTextEdits": [{"newText": "from collections import OrderedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import OrderedDict\n", "kind": 7, "label": "OrderedDict", "sortText": "4a.9999.OrderedDict"}, {"additionalTextEdits": [{"newText": "from requests.sessions import OrderedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import OrderedDict\n", "kind": 6, "label": "OrderedDict", "sortText": "4a.9999.OrderedDict"}, {"additionalTextEdits": [{"newText": "from requests.utils import OrderedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import OrderedDict\n", "kind": 6, "label": "OrderedDict", "sortText": "4a.9999.OrderedDict"}, {"additionalTextEdits": [{"newText": "from typing_extensions import OrderedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing_extensions import OrderedDict\n", "kind": 6, "label": "OrderedDict", "sortText": "4a.9999.OrderedDict"}, {"additionalTextEdits": [{"newText": "from http.client import PERMANENT_REDIRECT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.client import PERMANENT_REDIRECT\n", "kind": 6, "label": "PERMANENT_REDIRECT", "sortText": "4a.9999.PERMANENT_REDIRECT"}, {"additionalTextEdits": [{"newText": "from click.types import ParamTypeInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import ParamTypeInfoDict\n", "kind": 7, "label": "ParamTypeInfoDict", "sortText": "4a.9999.ParamTypeInfoDict"}, {"additionalTextEdits": [{"newText": "from flask.json.tag import PassDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.json.tag import PassDict\n", "kind": 7, "label": "PassDict", "sortText": "4a.9999.PassDict"}, {"additionalTextEdits": [{"newText": "from click.types import PathInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import PathInfoDict\n", "kind": 7, "label": "PathInfoDict", "sortText": "4a.9999.PathInfoDict"}, {"additionalTextEdits": [{"newText": "from requests.models import REDIRECT_STATI\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.models import REDIRECT_STATI\n", "kind": 6, "label": "REDIRECT_STATI", "sortText": "4a.9999.REDIRECT_STATI"}, {"additionalTextEdits": [{"newText": "from requests.sessions import REDIRECT_STATI\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import REDIRECT_STATI\n", "kind": 6, "label": "REDIRECT_STATI", "sortText": "4a.9999.REDIRECT_STATI"}, {"additionalTextEdits": [{"newText": "from uuid import RESERVED_MICROSOFT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from uuid import RESERVED_MICROSOFT\n", "kind": 6, "label": "RESERVED_MICROSOFT", "sortText": "4a.9999.RESERVED_MICROSOFT"}, {"additionalTextEdits": [{"newText": "from http.client import RemoteDisconnected\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.client import RemoteDisconnected\n", "kind": 7, "label": "RemoteDisconnected", "sortText": "4a.9999.RemoteDisconnected"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing import RequestAliasRedirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing import RequestAliasRedirect\n", "kind": 7, "label": "RequestAliasRedirect", "sortText": "4a.9999.RequestAliasRedirect"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.exceptions import RequestAliasRedirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.exceptions import RequestAliasRedirect\n", "kind": 7, "label": "RequestAliasRedirect", "sortText": "4a.9999.RequestAliasRedirect"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing import RequestRedirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing import RequestRedirect\n", "kind": 7, "label": "RequestRedirect", "sortText": "4a.9999.RequestRedirect"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.exceptions import RequestRedirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.exceptions import RequestRedirect\n", "kind": 7, "label": "RequestRedirect", "sortText": "4a.9999.RequestRedirect"}, {"additionalTextEdits": [{"newText": "from requests.sessions import SessionRedirectMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import SessionRedirectMixin\n", "kind": 7, "label": "SessionRedirectMixin", "sortText": "4a.9999.SessionRedirectMixin"}, {"additionalTextEdits": [{"newText": "from http.client import TEMPORARY_REDIRECT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.client import TEMPORARY_REDIRECT\n", "kind": 6, "label": "TEMPORARY_REDIRECT", "sortText": "4a.9999.TEMPORARY_REDIRECT"}, {"additionalTextEdits": [{"newText": "from flask.json.tag import TagDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.json.tag import TagDict\n", "kind": 7, "label": "TagDict", "sortText": "4a.9999.TagDict"}, {"additionalTextEdits": [{"newText": "from tempfile import TemporaryDirectory\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from tempfile import TemporaryDirectory\n", "kind": 7, "label": "TemporaryDirectory", "sortText": "4a.9999.TemporaryDirectory"}, {"additionalTextEdits": [{"newText": "from requests import TooManyRedirects\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests import TooManyRedirects\n", "kind": 7, "label": "TooManyRedirects", "sortText": "4a.9999.TooManyRedirects"}, {"additionalTextEdits": [{"newText": "from requests.exceptions import TooManyRedirects\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.exceptions import TooManyRedirects\n", "kind": 7, "label": "TooManyRedirects", "sortText": "4a.9999.TooManyRedirects"}, {"additionalTextEdits": [{"newText": "from requests.sessions import TooManyRedirects\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import TooManyRedirects\n", "kind": 6, "label": "TooManyRedirects", "sortText": "4a.9999.TooManyRedirects"}, {"additionalTextEdits": [{"newText": "from click.types import TupleInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import TupleInfoDict\n", "kind": 7, "label": "TupleInfoDict", "sortText": "4a.9999.TupleInfoDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import TypeConversionDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import TypeConversionDict\n", "kind": 7, "label": "TypeConversionDict", "sortText": "4a.9999.TypeConversionDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import TypeConversionDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import TypeConversionDict\n", "kind": 7, "label": "TypeConversionDict", "sortText": "4a.9999.TypeConversionDict"}, {"additionalTextEdits": [{"newText": "from typing import TypedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import TypedDict\n", "kind": 6, "label": "TypedDict", "sortText": "4a.9999.TypedDict"}, {"additionalTextEdits": [{"newText": "from typing_extensions import TypedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing_extensions import TypedDict\n", "kind": 6, "label": "TypedDict", "sortText": "4a.9999.TypedDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import UpdateDictMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import UpdateDictMixin\n", "kind": 7, "label": "UpdateDictMixin", "sortText": "4a.9999.UpdateDictMixin"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.mixins import UpdateDictMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.mixins import UpdateDictMixin\n", "kind": 7, "label": "UpdateDictMixin", "sortText": "4a.9999.UpdateDictMixin"}, {"additionalTextEdits": [{"newText": "from collections import UserDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import UserDict\n", "kind": 7, "label": "UserDict", "sortText": "4a.9999.UserDict"}, {"additionalTextEdits": [{"newText": "from weakref import WeakKeyDictionary\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from weakref import WeakKeyDictionary\n", "kind": 7, "label": "WeakKeyDictionary", "sortText": "4a.9999.WeakKeyDictionary"}, {"additionalTextEdits": [{"newText": "from weakref import WeakValueDictionary\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from weakref import WeakValueDictionary\n", "kind": 7, "label": "WeakValueDictionary", "sortText": "4a.9999.WeakValueDictionary"}, {"additionalTextEdits": [{"newText": "from codecs import _BufferedIncrementalDecoder\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from codecs import _BufferedIncrementalDecoder\n", "kind": 7, "label": "_BufferedIncrementalDecoder", "sortText": "4a.9999._BufferedIncrementalDecoder"}, {"additionalTextEdits": [{"newText": "from enum import _EnumDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from enum import _EnumDict\n", "kind": 7, "label": "_EnumDict", "sortText": "4a.9999._EnumDict"}, {"additionalTextEdits": [{"newText": "from inspect import _GetMembersPredicate\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from inspect import _GetMembersPredicate\n", "kind": 6, "label": "_GetMembersPredicate", "sortText": "4a.9999._GetMembersPredicate"}, {"additionalTextEdits": [{"newText": "from inspect import _GetMembersPredicateTypeGuard\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from inspect import _GetMembersPredicateTypeGuard\n", "kind": 6, "label": "_GetMembersPredicateTypeGuard", "sortText": "4a.9999._GetMembersPredicateTypeGuard"}, {"additionalTextEdits": [{"newText": "from inspect import _GetMembersPredicateTypeIs\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from inspect import _GetMembersPredicateTypeIs\n", "kind": 6, "label": "_GetMembersPredicateTypeIs", "sortText": "4a.9999._GetMembersPredicateTypeIs"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import _ImmutableOrderedMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import _ImmutableOrderedMultiDict\n", "kind": 7, "label": "_ImmutableOrderedMultiDict", "sortText": "4a.9999._ImmutableOrderedMultiDict"}, {"additionalTextEdits": [{"newText": "from collections import _OrderedDictItemsView\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import _OrderedDictItemsView\n", "kind": 7, "label": "_OrderedDictItemsView", "sortText": "4a.9999._OrderedDictItemsView"}, {"additionalTextEdits": [{"newText": "from collections import _OrderedDictKeysView\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import _OrderedDictKeysView\n", "kind": 7, "label": "_OrderedDictKeysView", "sortText": "4a.9999._OrderedDictKeysView"}, {"additionalTextEdits": [{"newText": "from collections import _OrderedDictValuesView\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import _OrderedDictValuesView\n", "kind": 7, "label": "_OrderedDictValuesView", "sortText": "4a.9999._OrderedDictValuesView"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import _OrderedMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import _OrderedMultiDict\n", "kind": 7, "label": "_OrderedMultiDict", "sortText": "4a.9999._OrderedMultiDict"}, {"additionalTextEdits": [{"newText": "from ssl import _PeerCertRetDictType\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import _PeerCertRetDictType\n", "kind": 6, "label": "_PeerCertRetDictType", "sortText": "4a.9999._PeerCertRetDictType"}, {"additionalTextEdits": [{"newText": "from itertools import _Predicate\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itertools import _Predicate\n", "kind": 6, "label": "_Predicate", "sortText": "4a.9999._Predicate"}, {"additionalTextEdits": [{"newText": "from contextlib import _RedirectStream\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextlib import _RedirectStream\n", "kind": 7, "label": "_RedirectStream", "sortText": "4a.9999._RedirectStream"}, {"additionalTextEdits": [{"newText": "from click.shell_completion import _SourceVarsDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.shell_completion import _SourceVarsDict\n", "kind": 7, "label": "_SourceVarsDict", "sortText": "4a.9999._SourceVarsDict"}, {"additionalTextEdits": [{"newText": "from contextlib import _SupportsRedirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextlib import _SupportsRedirect\n", "kind": 7, "label": "_SupportsRedirect", "sortText": "4a.9999._SupportsRedirect"}, {"additionalTextEdits": [{"newText": "from contextlib import _SupportsRedirectT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextlib import _SupportsRedirectT\n", "kind": 6, "label": "_SupportsRedirectT", "sortText": "4a.9999._SupportsRedirectT"}, {"additionalTextEdits": [{"newText": "from werkzeug.test import _TAnyMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.test import _TAnyMultiDict\n", "kind": 6, "label": "_TAnyMultiDict", "sortText": "4a.9999._TAnyMultiDict"}, {"additionalTextEdits": [{"newText": "from urllib3.util.ssl_ import _TYPE_PEER_CERT_RET_DICT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.ssl_ import _TYPE_PEER_CERT_RET_DICT\n", "kind": 7, "label": "_TYPE_PEER_CERT_RET_DICT", "sortText": "4a.9999._TYPE_PEER_CERT_RET_DICT"}, {"additionalTextEdits": [{"newText": "from typing import _TypedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import _TypedDict\n", "kind": 7, "label": "_TypedDict", "sortText": "4a.9999._TypedDict"}, {"additionalTextEdits": [{"newText": "from typing_extensions import _TypedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing_extensions import _TypedDict\n", "kind": 7, "label": "_TypedDict", "sortText": "4a.9999._TypedDict"}, {"additionalTextEdits": [{"newText": "from colorama.ansitowin32 import _WinTermCallDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from colorama.ansitowin32 import _WinTermCallDict\n", "kind": 6, "label": "_WinTermCallDict", "sortText": "4a.9999._WinTermCallDict"}, {"additionalTextEdits": [{"newText": "from urllib3.connection import _WrappedAndVerifiedSocket\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.connection import _WrappedAndVerifiedSocket\n", "kind": 7, "label": "_WrappedAndVerifiedSocket", "sortText": "4a.9999._WrappedAndVerifiedSocket"}, {"additionalTextEdits": [{"newText": "from abc import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from abc import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from ast import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ast import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.base_events import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.base_events import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.coroutines import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.coroutines import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.events import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.events import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.futures import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.futures import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.locks import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.locks import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.protocols import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.protocols import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.queues import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.queues import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.runners import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.runners import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.streams import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.streams import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.subprocess import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.subprocess import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.taskgroups import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.taskgroups import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.tasks import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.tasks import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.threads import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.threads import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.timeouts import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.timeouts import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.transports import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.transports import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.unix_events import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.unix_events import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from atexit import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from atexit import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from base64 import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from base64 import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from binascii import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from binascii import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from blinker import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from blinker import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from blinker.base import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from blinker.base import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.core import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.core import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.decorators import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.decorators import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.formatting import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.formatting import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.globals import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.globals import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.parser import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.parser import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.shell_completion import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.shell_completion import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.termui import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.termui import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.testing import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.testing import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.types import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from code import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from code import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from codecs import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from codecs import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from collections import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from collections.abc import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections.abc import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from colorama import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from colorama import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from colorama.ansitowin32 import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from colorama.ansitowin32 import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from contextlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from contextvars import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextvars import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from copy import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from copy import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from dataclasses import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from dataclasses import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from datetime import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from datetime import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from decimal import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from decimal import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from difflib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from difflib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from email.utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from email.utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from enum import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from enum import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from errno import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from errno import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.app import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.app import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.blueprints import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.blueprints import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.cli import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.cli import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.config import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.config import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.ctx import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.ctx import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.debughelpers import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.debughelpers import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.globals import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.globals import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.helpers import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.helpers import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.json import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.json import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.json.provider import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.json.provider import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.json.tag import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.json.tag import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.logging import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.logging import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.sansio.app import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.sansio.app import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.sansio.blueprints import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.sansio.blueprints import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.sansio.scaffold import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.sansio.scaffold import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.sessions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.sessions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.signals import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.signals import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.templating import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.templating import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.testing import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.testing import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.typing import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.typing import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.wrappers import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.wrappers import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from fnmatch import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from fnmatch import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from functools import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from functools import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from genericpath import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from genericpath import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from getpass import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from getpass import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from gettext import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from gettext import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from glob import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from glob import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from hashlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from hashlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from hmac import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from hmac import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from html import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from html import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from http import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from http.client import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.client import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from http.cookiejar import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.cookiejar import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from http.cookies import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.cookies import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from http.server import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.server import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from idna import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from idna import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from idna.core import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from idna.core import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib.abc import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib.abc import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib.machinery import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib.machinery import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib.metadata import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib.metadata import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib.util import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib.util import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from inspect import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from inspect import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from io import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from io import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itertools import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itertools import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous.encoding import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous.encoding import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous.exc import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous.exc import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous.serializer import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous.serializer import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous.signer import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous.signer import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous.timed import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous.timed import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous.url_safe import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous.url_safe import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2 import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2 import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.async_utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.async_utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.bccache import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.bccache import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.compiler import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.compiler import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.debug import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.debug import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.environment import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.environment import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.lexer import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.lexer import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.loaders import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.loaders import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.nodes import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.nodes import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.parser import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.parser import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.runtime import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.runtime import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from json import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from json import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from json.decoder import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from json.decoder import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from json.encoder import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from json.encoder import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from logging import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from logging import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from markupsafe import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from markupsafe import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from marshal import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from marshal import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from math import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from math import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from mimetypes import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from mimetypes import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from numbers import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from numbers import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from operator import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from operator import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from os import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from os import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from os.path import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from os.path import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from pathlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from pathlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from pickle import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from pickle import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from pkgutil import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from pkgutil import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from platform import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from platform import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from posixpath import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from posixpath import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from pprint import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from pprint import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from queue import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from queue import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from random import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from random import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from re import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from re import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from readline import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from readline import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.adapters import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.adapters import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.api import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.api import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.auth import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.auth import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.compat import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.compat import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.cookies import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.cookies import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.hooks import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.hooks import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.models import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.models import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.packages import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.packages import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.sessions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.status_codes import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.status_codes import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.structures import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.structures import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from rlcompleter import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from rlcompleter import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from secrets import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from secrets import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from selectors import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from selectors import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from shlex import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from shlex import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from shutil import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from shutil import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from socket import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from socket import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from socketserver import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from socketserver import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from socks import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from socks import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from ssl import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from stat import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from stat import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from string import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from string import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from subprocess import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from subprocess import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from sys import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from sys import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from tempfile import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from tempfile import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from termios import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from termios import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from threading import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from threading import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from time import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from time import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from traceback import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from traceback import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from tty import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from tty import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from types import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from types import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from typing import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from typing_extensions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing_extensions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from unicodedata import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from unicodedata import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib.parse import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib.parse import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3 import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3 import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.connection import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.connection import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.connectionpool import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.connectionpool import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.contrib.socks import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.contrib.socks import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.fields import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.fields import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.filepost import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.filepost import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.poolmanager import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.poolmanager import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.response import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.response import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.connection import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.connection import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.proxy import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.proxy import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.request import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.request import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.response import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.response import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.retry import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.retry import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.ssl_ import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.ssl_ import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.timeout import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.timeout import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.url import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.url import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.util import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.util import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from uuid import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from uuid import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from warnings import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from warnings import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from weakref import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from weakref import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from webbrowser import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from webbrowser import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.accept import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.accept import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.auth import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.auth import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.cache_control import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.cache_control import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.csp import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.csp import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.etag import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.etag import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.file_storage import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.file_storage import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.headers import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.headers import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.mixins import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.mixins import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.range import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.range import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.debug import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.debug import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.debug.tbtools import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.debug.tbtools import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.formparser import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.formparser import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.http import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.http import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.local import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.local import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.middleware.shared_data import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.middleware.shared_data import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.converters import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.converters import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.map import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.map import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.matcher import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.matcher import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.rules import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.rules import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.sansio import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.sansio import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.sansio.http import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.sansio.http import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.sansio.multipart import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.sansio.multipart import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.sansio.request import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.sansio.request import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.sansio.response import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.sansio.response import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.sansio.utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.sansio.utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.security import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.security import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.serving import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.serving import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.test import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.test import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.urls import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.urls import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.user_agent import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.user_agent import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.wrappers import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.wrappers import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.wrappers.request import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.wrappers.request import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.wrappers.response import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.wrappers.response import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.wsgi import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.wsgi import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from wsgiref.types import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from wsgiref.types import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from zipfile import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from zipfile import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from zipimport import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from zipimport import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from zlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from zlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from idna.core import _bidi_rtl_categories\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from idna.core import _bidi_rtl_categories\n", "kind": 6, "label": "_bidi_rtl_categories", "sortText": "4a.9999._bidi_rtl_categories"}, {"additionalTextEdits": [{"newText": "from jinja2.runtime import _dict_method_all\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.runtime import _dict_method_all\n", "kind": 3, "label": "_dict_method_all", "sortText": "4a.9999._dict_method_all"}, {"additionalTextEdits": [{"newText": "from importlib.util import _incompatible_extension_module_restrictions\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib.util import _incompatible_extension_module_restrictions\n", "kind": 7, "label": "_incompatible_extension_module_restrictions", "sortText": "4a.9999._incompatible_extension_module_restrictions"}, {"additionalTextEdits": [{"newText": "from collections import _odict_items\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import _odict_items\n", "kind": 7, "label": "_odict_items", "sortText": "4a.9999._odict_items"}, {"additionalTextEdits": [{"newText": "from collections import _odict_keys\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import _odict_keys\n", "kind": 7, "label": "_odict_keys", "sortText": "4a.9999._odict_keys"}, {"additionalTextEdits": [{"newText": "from collections import _odict_values\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import _odict_values\n", "kind": 7, "label": "_odict_values", "sortText": "4a.9999._odict_values"}, {"additionalTextEdits": [{"newText": "from flask.helpers import _wz_redirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.helpers import _wz_redirect\n", "kind": 3, "label": "_wz_redirect", "sortText": "4a.9999._wz_redirect"}, {"additionalTextEdits": [{"newText": "from flask.sansio.app import _wz_redirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.sansio.app import _wz_redirect\n", "kind": 3, "label": "_wz_redirect", "sortText": "4a.9999._wz_redirect"}, {"additionalTextEdits": [{"newText": "from requests.utils import add_dict_to_cookiejar\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import add_dict_to_cookiejar\n", "kind": 3, "label": "add_dict_to_cookiejar", "sortText": "4a.9999.add_dict_to_cookiejar"}, {"additionalTextEdits": [{"newText": "from werkzeug.utils import append_slash_redirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.utils import append_slash_redirect\n", "kind": 3, "label": "append_slash_redirect", "sortText": "4a.9999.append_slash_redirect"}, {"additionalTextEdits": [{"newText": "from dataclasses import asdict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from dataclasses import asdict\n", "kind": 3, "label": "asdict", "sortText": "4a.9999.asdict"}, {"additionalTextEdits": [{"newText": "from flask.debughelpers import attach_enctype_error_multidict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.debughelpers import attach_enctype_error_multidict\n", "kind": 3, "label": "attach_enctype_error_multidict", "sortText": "4a.9999.attach_enctype_error_multidict"}, {"additionalTextEdits": [{"newText": "from unicodedata import bidirectional\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from unicodedata import bidirectional\n", "kind": 3, "label": "bidirectional", "sortText": "4a.9999.bidirectional"}, {"additionalTextEdits": [{"newText": "from requests.cookies import cookiejar_from_dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.cookies import cookiejar_from_dict\n", "kind": 3, "label": "cookiejar_from_dict", "sortText": "4a.9999.cookiejar_from_dict"}, {"additionalTextEdits": [{"newText": "from requests.models import cookiejar_from_dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.models import cookiejar_from_dict\n", "kind": 6, "label": "cookiejar_from_dict", "sortText": "4a.9999.cookiejar_from_dict"}, {"additionalTextEdits": [{"newText": "from requests.sessions import cookiejar_from_dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import cookiejar_from_dict\n", "kind": 6, "label": "cookiejar_from_dict", "sortText": "4a.9999.cookiejar_from_dict"}, {"additionalTextEdits": [{"newText": "from requests.utils import cookiejar_from_dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import cookiejar_from_dict\n", "kind": 6, "label": "cookiejar_from_dict", "sortText": "4a.9999.cookiejar_from_dict"}, {"additionalTextEdits": [{"newText": "from sys import deactivate_stack_trampoline\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from sys import deactivate_stack_trampoline\n", "kind": 3, "label": "deactivate_stack_trampoline", "sortText": "4a.9999.deactivate_stack_trampoline"}, {"additionalTextEdits": [{"newText": "from collections import defaultdict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import defaultdict\n", "kind": 7, "label": "defaultdict", "sortText": "4a.9999.defaultdict"}, {"additionalTextEdits": [{"newText": "from requests.utils import dict_from_cookiejar\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import dict_from_cookiejar\n", "kind": 3, "label": "dict_from_cookiejar", "sortText": "4a.9999.dict_from_cookiejar"}, {"additionalTextEdits": [{"newText": "from requests.utils import dict_to_sequence\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import dict_to_sequence\n", "kind": 3, "label": "dict_to_sequence", "sortText": "4a.9999.dict_to_sequence"}, {"additionalTextEdits": [{"newText": "from requests.utils import get_encodings_from_content\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import get_encodings_from_content\n", "kind": 3, "label": "get_encodings_from_content", "sortText": "4a.9999.get_encodings_from_content"}, {"additionalTextEdits": [{"newText": "from typing import is_typeddict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import is_typeddict\n", "kind": 3, "label": "is_typeddict", "sortText": "4a.9999.is_typeddict"}, {"additionalTextEdits": [{"newText": "from requests.auth import parse_dict_header\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.auth import parse_dict_header\n", "kind": 6, "label": "parse_dict_header", "sortText": "4a.9999.parse_dict_header"}, {"additionalTextEdits": [{"newText": "from requests.utils import parse_dict_header\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import parse_dict_header\n", "kind": 3, "label": "parse_dict_header", "sortText": "4a.9999.parse_dict_header"}, {"additionalTextEdits": [{"newText": "from werkzeug.http import parse_dict_header\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.http import parse_dict_header\n", "kind": 3, "label": "parse_dict_header", "sortText": "4a.9999.parse_dict_header"}, {"additionalTextEdits": [{"newText": "from flask import redirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask import redirect\n", "kind": 3, "label": "redirect", "sortText": "4a.9999.redirect"}, {"additionalTextEdits": [{"newText": "from flask.helpers import redirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.helpers import redirect\n", "kind": 3, "label": "redirect", "sortText": "4a.9999.redirect"}, {"additionalTextEdits": [{"newText": "from werkzeug.utils import redirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.utils import redirect\n", "kind": 3, "label": "redirect", "sortText": "4a.9999.redirect"}, {"additionalTextEdits": [{"newText": "from contextlib import redirect_stderr\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextlib import redirect_stderr\n", "kind": 7, "label": "redirect_stderr", "sortText": "4a.9999.redirect_stderr"}, {"additionalTextEdits": [{"newText": "from contextlib import redirect_stdout\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextlib import redirect_stdout\n", "kind": 7, "label": "redirect_stdout", "sortText": "4a.9999.redirect_stdout"}, {"additionalTextEdits": [{"newText": "from flask import send_from_directory\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask import send_from_directory\n", "kind": 3, "label": "send_from_directory", "sortText": "4a.9999.send_from_directory"}, {"additionalTextEdits": [{"newText": "from flask.helpers import send_from_directory\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.helpers import send_from_directory\n", "kind": 3, "label": "send_from_directory", "sortText": "4a.9999.send_from_directory"}, {"additionalTextEdits": [{"newText": "from werkzeug.utils import send_from_directory\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.utils import send_from_directory\n", "kind": 3, "label": "send_from_directory", "sortText": "4a.9999.send_from_directory"}, {"additionalTextEdits": [{"newText": "from functools import singledispatchmethod\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from functools import singledispatchmethod\n", "kind": 7, "label": "singledispatchmethod", "sortText": "4a.9999.singledispatchmethod"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE\n", "kind": 6, "label": "ALERT_DESCRIPTION_BAD_CERTIFICATE", "sortText": "4b.9999.ALERT_DESCRIPTION_BAD_CERTIFICATE"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE\n", "kind": 6, "label": "ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE", "sortText": "4b.9999.ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE\n", "kind": 6, "label": "ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE", "sortText": "4b.9999.ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_EXPIRED\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_EXPIRED\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_EXPIRED", "sortText": "4b.9999.ALERT_DESCRIPTION_CERTIFICATE_EXPIRED"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_REVOKED\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_REVOKED\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_REVOKED", "sortText": "4b.9999.ALERT_DESCRIPTION_CERTIFICATE_REVOKED"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN", "sortText": "4b.9999.ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE", "sortText": "4b.9999.ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_CLOSE_NOTIFY\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_CLOSE_NOTIFY\n", "kind": 6, "label": "ALERT_DESCRIPTION_CLOSE_NOTIFY", "sortText": "4b.9999.ALERT_DESCRIPTION_CLOSE_NOTIFY"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_DECRYPT_ERROR\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_DECRYPT_ERROR\n", "kind": 6, "label": "ALERT_DESCRIPTION_DECRYPT_ERROR", "sortText": "4b.9999.ALERT_DESCRIPTION_DECRYPT_ERROR"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_INSUFFICIENT_SECURITY\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_INSUFFICIENT_SECURITY\n", "kind": 6, "label": "ALERT_DESCRIPTION_INSUFFICIENT_SECURITY", "sortText": "4b.9999.ALERT_DESCRIPTION_INSUFFICIENT_SECURITY"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_UNEXPECTED_MESSAGE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_UNEXPECTED_MESSAGE\n", "kind": 6, "label": "ALERT_DESCRIPTION_UNEXPECTED_MESSAGE", "sortText": "4b.9999.ALERT_DESCRIPTION_UNEXPECTED_MESSAGE"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE\n", "kind": 6, "label": "ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE", "sortText": "4b.9999.ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE"}, {"additionalTextEdits": [{"newText": "from urllib3._collections import HTTPHeaderDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._collections import HTTPHeaderDict\n", "kind": 7, "label": "HTTPHeaderDict", "sortText": "4b.9999.HTTPHeaderDict"}, {"additionalTextEdits": [{"newText": "from urllib3._collections import HTTPHeaderDictItemView\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._collections import HTTPHeaderDictItemView\n", "kind": 7, "label": "HTTPHeaderDictItemView", "sortText": "4b.9999.HTTPHeaderDictItemView"}, {"additionalTextEdits": [{"newText": "from _socket import IP_DEFAULT_MULTICAST_LOOP\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _socket import IP_DEFAULT_MULTICAST_LOOP\n", "kind": 6, "label": "IP_DEFAULT_MULTICAST_LOOP", "sortText": "4b.9999.IP_DEFAULT_MULTICAST_LOOP"}, {"additionalTextEdits": [{"newText": "from _socket import IP_DEFAULT_MULTICAST_TTL\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _socket import IP_DEFAULT_MULTICAST_TTL\n", "kind": 6, "label": "IP_DEFAULT_MULTICAST_TTL", "sortText": "4b.9999.IP_DEFAULT_MULTICAST_TTL"}, {"additionalTextEdits": [{"newText": "from _typeshed import IdentityFunction\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _typeshed import IdentityFunction\n", "kind": 7, "label": "IdentityFunction", "sortText": "4b.9999.IdentityFunction"}, {"additionalTextEdits": [{"newText": "from _typeshed._type_checker_internals import TypedDictFallback\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _typeshed._type_checker_internals import TypedDictFallback\n", "kind": 7, "label": "TypedDictFallback", "sortText": "4b.9999.TypedDictFallback"}, {"additionalTextEdits": [{"newText": "from werkzeug._internal import _DictAccessorProperty\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug._internal import _DictAccessorProperty\n", "kind": 7, "label": "_DictAccessorProperty", "sortText": "4b.9999._DictAccessorProperty"}, {"additionalTextEdits": [{"newText": "from _ssl import _PeerCertRetDictType\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import _PeerCertRetDictType\n", "kind": 6, "label": "_PeerCertRetDictType", "sortText": "4b.9999._PeerCertRetDictType"}, {"additionalTextEdits": [{"newText": "from __future__ import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from __future__ import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _codecs import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _codecs import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _collections_abc import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _collections_abc import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _contextvars import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _contextvars import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _frozen_importlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _frozen_importlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _frozen_importlib_external import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _frozen_importlib_external import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _hashlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _hashlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _io import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _io import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _operator import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _operator import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _pickle import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _pickle import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _random import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _random import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _socket import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _socket import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _ssl import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _stat import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _stat import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _thread import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _thread import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _typeshed import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _typeshed import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _typeshed._type_checker_internals import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _typeshed._type_checker_internals import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _typeshed.wsgi import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _typeshed.wsgi import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _warnings import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _warnings import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _weakref import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _weakref import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from blinker._utilities import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from blinker._utilities import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click._compat import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click._compat import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click._termui_impl import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click._termui_impl import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click._textwrap import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click._textwrap import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click._utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click._utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib._abc import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib._abc import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib._bootstrap import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib._bootstrap import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib._bootstrap_external import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib._bootstrap_external import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib.metadata._meta import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib.metadata._meta import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from markupsafe._native import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from markupsafe._native import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from markupsafe._speedups import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from markupsafe._speedups import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.__version__ import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.__version__ import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3._base_connection import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._base_connection import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3._collections import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._collections import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3._request_methods import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._request_methods import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3._version import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._version import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug._internal import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug._internal import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug._reloader import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug._reloader import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _collections_abc import dict_items\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _collections_abc import dict_items\n", "kind": 7, "label": "dict_items", "sortText": "4b.9999.dict_items"}, {"additionalTextEdits": [{"newText": "from _collections_abc import dict_keys\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _collections_abc import dict_keys\n", "kind": 7, "label": "dict_keys", "sortText": "4b.9999.dict_keys"}, {"additionalTextEdits": [{"newText": "from _collections_abc import dict_values\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _collections_abc import dict_values\n", "kind": 7, "label": "dict_values", "sortText": "4b.9999.dict_values"}, {"additionalTextEdits": [{"newText": "from urllib3._collections import ensure_can_construct_http_header_dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._collections import ensure_can_construct_http_header_dict\n", "kind": 3, "label": "ensure_can_construct_http_header_dict", "sortText": "4b.9999.ensure_can_construct_http_header_dict"}, {"additionalTextEdits": [{"newText": "from typing import DefaultDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import DefaultDict\n", "kind": 6, "label": "DefaultDict", "sortText": "9.9999.DefaultDict", "tags": [1]}, {"additionalTextEdits": [{"newText": "from typing import Dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import Dict\n", "kind": 6, "label": "Dict", "sortText": "9.9999.Dict", "tags": [1]}, {"additionalTextEdits": [{"newText": "from typing import OrderedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import OrderedDict\n", "kind": 6, "label": "OrderedDict", "sortText": "9.9999.OrderedDict", "tags": [1]}]}} {"suite": "web", "label": "request args completion", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 9, "character": 18, "iteration": 5, "result": {"isIncomplete": false, "items": [{"kind": 14, "label": "False", "sortText": "0.9999.False"}, {"data": "builtin", "kind": 7, "label": "IsADirectoryError", "sortText": "0.9999.IsADirectoryError"}, {"kind": 14, "label": "None", "sortText": "0.9999.None"}, {"data": "builtin", "kind": 7, "label": "NotADirectoryError", "sortText": "0.9999.NotADirectoryError"}, {"data": "builtin", "kind": 7, "label": "PendingDeprecationWarning", "sortText": "0.9999.PendingDeprecationWarning"}, {"kind": 14, "label": "True", "sortText": "0.9999.True"}, {"kind": 14, "label": "and", "sortText": "0.9999.and"}, {"kind": 14, "label": "await", "sortText": "0.9999.await"}, {"data": "builtin", "kind": 7, "label": "dict", "sortText": "0.9999.dict"}, {"kind": 14, "label": "else", "sortText": "0.9999.else"}, {"kind": 14, "label": "for", "sortText": "0.9999.for"}, {"kind": 14, "label": "if", "sortText": "0.9999.if"}, {"kind": 14, "label": "in", "sortText": "0.9999.in"}, {"kind": 14, "label": "is", "sortText": "0.9999.is"}, {"kind": 14, "label": "lambda", "sortText": "0.9999.lambda"}, {"kind": 14, "label": "not", "sortText": "0.9999.not"}, {"kind": 14, "label": "or", "sortText": "0.9999.or"}, {"kind": 14, "label": "yield", "sortText": "0.9999.yield"}, {"data": "builtin", "kind": 21, "label": "__dict__", "sortText": "3.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE\n", "kind": 6, "label": "ALERT_DESCRIPTION_BAD_CERTIFICATE", "sortText": "4a.9999.ALERT_DESCRIPTION_BAD_CERTIFICATE"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE\n", "kind": 6, "label": "ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE", "sortText": "4a.9999.ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE\n", "kind": 6, "label": "ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE", "sortText": "4a.9999.ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_EXPIRED\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_EXPIRED\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_EXPIRED", "sortText": "4a.9999.ALERT_DESCRIPTION_CERTIFICATE_EXPIRED"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_REVOKED\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_REVOKED\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_REVOKED", "sortText": "4a.9999.ALERT_DESCRIPTION_CERTIFICATE_REVOKED"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN", "sortText": "4a.9999.ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE", "sortText": "4a.9999.ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_CLOSE_NOTIFY\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_CLOSE_NOTIFY\n", "kind": 6, "label": "ALERT_DESCRIPTION_CLOSE_NOTIFY", "sortText": "4a.9999.ALERT_DESCRIPTION_CLOSE_NOTIFY"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_DECRYPT_ERROR\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_DECRYPT_ERROR\n", "kind": 6, "label": "ALERT_DESCRIPTION_DECRYPT_ERROR", "sortText": "4a.9999.ALERT_DESCRIPTION_DECRYPT_ERROR"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_INSUFFICIENT_SECURITY\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_INSUFFICIENT_SECURITY\n", "kind": 6, "label": "ALERT_DESCRIPTION_INSUFFICIENT_SECURITY", "sortText": "4a.9999.ALERT_DESCRIPTION_INSUFFICIENT_SECURITY"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_UNEXPECTED_MESSAGE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_UNEXPECTED_MESSAGE\n", "kind": 6, "label": "ALERT_DESCRIPTION_UNEXPECTED_MESSAGE", "sortText": "4a.9999.ALERT_DESCRIPTION_UNEXPECTED_MESSAGE"}, {"additionalTextEdits": [{"newText": "from ssl import ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE\n", "kind": 6, "label": "ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE", "sortText": "4a.9999.ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE"}, {"additionalTextEdits": [{"newText": "from urllib3.exceptions import BodyNotHttplibCompatible\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.exceptions import BodyNotHttplibCompatible\n", "kind": 7, "label": "BodyNotHttplibCompatible", "sortText": "4a.9999.BodyNotHttplibCompatible"}, {"additionalTextEdits": [{"newText": "from codecs import BufferedIncrementalDecoder\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from codecs import BufferedIncrementalDecoder\n", "kind": 7, "label": "BufferedIncrementalDecoder", "sortText": "4a.9999.BufferedIncrementalDecoder"}, {"additionalTextEdits": [{"newText": "from codecs import BufferedIncrementalEncoder\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from codecs import BufferedIncrementalEncoder\n", "kind": 7, "label": "BufferedIncrementalEncoder", "sortText": "4a.9999.BufferedIncrementalEncoder"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import CallbackDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import CallbackDict\n", "kind": 7, "label": "CallbackDict", "sortText": "4a.9999.CallbackDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import CallbackDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import CallbackDict\n", "kind": 7, "label": "CallbackDict", "sortText": "4a.9999.CallbackDict"}, {"additionalTextEdits": [{"newText": "from requests.structures import CaseInsensitiveDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.structures import CaseInsensitiveDict\n", "kind": 7, "label": "CaseInsensitiveDict", "sortText": "4a.9999.CaseInsensitiveDict"}, {"additionalTextEdits": [{"newText": "from requests.utils import CaseInsensitiveDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import CaseInsensitiveDict\n", "kind": 6, "label": "CaseInsensitiveDict", "sortText": "4a.9999.CaseInsensitiveDict"}, {"additionalTextEdits": [{"newText": "from click.types import ChoiceInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import ChoiceInfoDict\n", "kind": 7, "label": "ChoiceInfoDict", "sortText": "4a.9999.ChoiceInfoDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.exceptions import ClientDisconnected\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.exceptions import ClientDisconnected\n", "kind": 7, "label": "ClientDisconnected", "sortText": "4a.9999.ClientDisconnected"}, {"additionalTextEdits": [{"newText": "from werkzeug.test import ClientRedirectError\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.test import ClientRedirectError\n", "kind": 7, "label": "ClientRedirectError", "sortText": "4a.9999.ClientRedirectError"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import CombinedMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import CombinedMultiDict\n", "kind": 7, "label": "CombinedMultiDict", "sortText": "4a.9999.CombinedMultiDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import CombinedMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import CombinedMultiDict\n", "kind": 7, "label": "CombinedMultiDict", "sortText": "4a.9999.CombinedMultiDict"}, {"additionalTextEdits": [{"newText": "from requests.models import DEFAULT_REDIRECT_LIMIT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.models import DEFAULT_REDIRECT_LIMIT\n", "kind": 6, "label": "DEFAULT_REDIRECT_LIMIT", "sortText": "4a.9999.DEFAULT_REDIRECT_LIMIT"}, {"additionalTextEdits": [{"newText": "from requests.sessions import DEFAULT_REDIRECT_LIMIT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import DEFAULT_REDIRECT_LIMIT\n", "kind": 6, "label": "DEFAULT_REDIRECT_LIMIT", "sortText": "4a.9999.DEFAULT_REDIRECT_LIMIT"}, {"additionalTextEdits": [{"newText": "from pickle import DICT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from pickle import DICT\n", "kind": 6, "label": "DICT", "sortText": "4a.9999.DICT"}, {"additionalTextEdits": [{"newText": "from click.types import DateTimeInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import DateTimeInfoDict\n", "kind": 7, "label": "DateTimeInfoDict", "sortText": "4a.9999.DateTimeInfoDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.debug import DebuggedApplication\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.debug import DebuggedApplication\n", "kind": 7, "label": "DebuggedApplication", "sortText": "4a.9999.DebuggedApplication"}, {"additionalTextEdits": [{"newText": "from decimal import DecimalException\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from decimal import DecimalException\n", "kind": 7, "label": "DecimalException", "sortText": "4a.9999.DecimalException"}, {"additionalTextEdits": [{"newText": "from jinja2.nodes import DerivedContextReference\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.nodes import DerivedContextReference\n", "kind": 7, "label": "DerivedContextReference", "sortText": "4a.9999.DerivedContextReference"}, {"additionalTextEdits": [{"newText": "from ast import Dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ast import Dict\n", "kind": 7, "label": "Dict", "sortText": "4a.9999.Dict"}, {"additionalTextEdits": [{"newText": "from jinja2.nodes import Dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.nodes import Dict\n", "kind": 7, "label": "Dict", "sortText": "4a.9999.Dict"}, {"additionalTextEdits": [{"newText": "from ast import DictComp\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ast import DictComp\n", "kind": 7, "label": "DictComp", "sortText": "4a.9999.DictComp"}, {"additionalTextEdits": [{"newText": "from jinja2 import DictLoader\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2 import DictLoader\n", "kind": 7, "label": "DictLoader", "sortText": "4a.9999.DictLoader"}, {"additionalTextEdits": [{"newText": "from jinja2.loaders import DictLoader\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.loaders import DictLoader\n", "kind": 7, "label": "DictLoader", "sortText": "4a.9999.DictLoader"}, {"additionalTextEdits": [{"newText": "from types import DynamicClassAttribute\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from types import DynamicClassAttribute\n", "kind": 7, "label": "DynamicClassAttribute", "sortText": "4a.9999.DynamicClassAttribute"}, {"additionalTextEdits": [{"newText": "from pickle import EMPTY_DICT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from pickle import EMPTY_DICT\n", "kind": 6, "label": "EMPTY_DICT", "sortText": "4a.9999.EMPTY_DICT"}, {"additionalTextEdits": [{"newText": "from stat import FILE_ATTRIBUTE_DIRECTORY\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from stat import FILE_ATTRIBUTE_DIRECTORY\n", "kind": 6, "label": "FILE_ATTRIBUTE_DIRECTORY", "sortText": "4a.9999.FILE_ATTRIBUTE_DIRECTORY"}, {"additionalTextEdits": [{"newText": "from click.types import FileInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import FileInfoDict\n", "kind": 7, "label": "FileInfoDict", "sortText": "4a.9999.FileInfoDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import FileMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import FileMultiDict\n", "kind": 7, "label": "FileMultiDict", "sortText": "4a.9999.FileMultiDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.file_storage import FileMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.file_storage import FileMultiDict\n", "kind": 7, "label": "FileMultiDict", "sortText": "4a.9999.FileMultiDict"}, {"additionalTextEdits": [{"newText": "from flask.debughelpers import FormDataRoutingRedirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.debughelpers import FormDataRoutingRedirect\n", "kind": 7, "label": "FormDataRoutingRedirect", "sortText": "4a.9999.FormDataRoutingRedirect"}, {"additionalTextEdits": [{"newText": "from click.types import FuncParamTypeInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import FuncParamTypeInfoDict\n", "kind": 7, "label": "FuncParamTypeInfoDict", "sortText": "4a.9999.FuncParamTypeInfoDict"}, {"additionalTextEdits": [{"newText": "from urllib3 import HTTPHeaderDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3 import HTTPHeaderDict\n", "kind": 7, "label": "HTTPHeaderDict", "sortText": "4a.9999.HTTPHeaderDict"}, {"additionalTextEdits": [{"newText": "from socket import IP_DEFAULT_MULTICAST_LOOP\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from socket import IP_DEFAULT_MULTICAST_LOOP\n", "kind": 6, "label": "IP_DEFAULT_MULTICAST_LOOP", "sortText": "4a.9999.IP_DEFAULT_MULTICAST_LOOP"}, {"additionalTextEdits": [{"newText": "from socket import IP_DEFAULT_MULTICAST_TTL\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from socket import IP_DEFAULT_MULTICAST_TTL\n", "kind": 6, "label": "IP_DEFAULT_MULTICAST_TTL", "sortText": "4a.9999.IP_DEFAULT_MULTICAST_TTL"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import ImmutableDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import ImmutableDict\n", "kind": 7, "label": "ImmutableDict", "sortText": "4a.9999.ImmutableDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import ImmutableDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import ImmutableDict\n", "kind": 7, "label": "ImmutableDict", "sortText": "4a.9999.ImmutableDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import ImmutableDictMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import ImmutableDictMixin\n", "kind": 7, "label": "ImmutableDictMixin", "sortText": "4a.9999.ImmutableDictMixin"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.mixins import ImmutableDictMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.mixins import ImmutableDictMixin\n", "kind": 7, "label": "ImmutableDictMixin", "sortText": "4a.9999.ImmutableDictMixin"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import ImmutableMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import ImmutableMultiDict\n", "kind": 7, "label": "ImmutableMultiDict", "sortText": "4a.9999.ImmutableMultiDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import ImmutableMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import ImmutableMultiDict\n", "kind": 7, "label": "ImmutableMultiDict", "sortText": "4a.9999.ImmutableMultiDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import ImmutableMultiDictMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import ImmutableMultiDictMixin\n", "kind": 7, "label": "ImmutableMultiDictMixin", "sortText": "4a.9999.ImmutableMultiDictMixin"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.mixins import ImmutableMultiDictMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.mixins import ImmutableMultiDictMixin\n", "kind": 7, "label": "ImmutableMultiDictMixin", "sortText": "4a.9999.ImmutableMultiDictMixin"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import ImmutableTypeConversionDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import ImmutableTypeConversionDict\n", "kind": 7, "label": "ImmutableTypeConversionDict", "sortText": "4a.9999.ImmutableTypeConversionDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import ImmutableTypeConversionDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import ImmutableTypeConversionDict\n", "kind": 7, "label": "ImmutableTypeConversionDict", "sortText": "4a.9999.ImmutableTypeConversionDict"}, {"additionalTextEdits": [{"newText": "from idna import InvalidCodepointContext\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from idna import InvalidCodepointContext\n", "kind": 7, "label": "InvalidCodepointContext", "sortText": "4a.9999.InvalidCodepointContext"}, {"additionalTextEdits": [{"newText": "from idna.core import InvalidCodepointContext\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from idna.core import InvalidCodepointContext\n", "kind": 7, "label": "InvalidCodepointContext", "sortText": "4a.9999.InvalidCodepointContext"}, {"additionalTextEdits": [{"newText": "from requests.structures import LookupDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.structures import LookupDict\n", "kind": 7, "label": "LookupDict", "sortText": "4a.9999.LookupDict"}, {"additionalTextEdits": [{"newText": "from http.client import MISDIRECTED_REQUEST\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.client import MISDIRECTED_REQUEST\n", "kind": 6, "label": "MISDIRECTED_REQUEST", "sortText": "4a.9999.MISDIRECTED_REQUEST"}, {"additionalTextEdits": [{"newText": "from werkzeug.exceptions import MisdirectedRequest\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.exceptions import MisdirectedRequest\n", "kind": 7, "label": "MisdirectedRequest", "sortText": "4a.9999.MisdirectedRequest"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import MultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import MultiDict\n", "kind": 7, "label": "MultiDict", "sortText": "4a.9999.MultiDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import MultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import MultiDict\n", "kind": 7, "label": "MultiDict", "sortText": "4a.9999.MultiDict"}, {"additionalTextEdits": [{"newText": "from click.types import NumberRangeInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import NumberRangeInfoDict\n", "kind": 7, "label": "NumberRangeInfoDict", "sortText": "4a.9999.NumberRangeInfoDict"}, {"additionalTextEdits": [{"newText": "from os import O_DIRECT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from os import O_DIRECT\n", "kind": 6, "label": "O_DIRECT", "sortText": "4a.9999.O_DIRECT"}, {"additionalTextEdits": [{"newText": "from os import O_DIRECTORY\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from os import O_DIRECTORY\n", "kind": 6, "label": "O_DIRECTORY", "sortText": "4a.9999.O_DIRECTORY"}, {"additionalTextEdits": [{"newText": "from collections import OrderedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import OrderedDict\n", "kind": 7, "label": "OrderedDict", "sortText": "4a.9999.OrderedDict"}, {"additionalTextEdits": [{"newText": "from requests.sessions import OrderedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import OrderedDict\n", "kind": 6, "label": "OrderedDict", "sortText": "4a.9999.OrderedDict"}, {"additionalTextEdits": [{"newText": "from requests.utils import OrderedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import OrderedDict\n", "kind": 6, "label": "OrderedDict", "sortText": "4a.9999.OrderedDict"}, {"additionalTextEdits": [{"newText": "from typing_extensions import OrderedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing_extensions import OrderedDict\n", "kind": 6, "label": "OrderedDict", "sortText": "4a.9999.OrderedDict"}, {"additionalTextEdits": [{"newText": "from http.client import PERMANENT_REDIRECT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.client import PERMANENT_REDIRECT\n", "kind": 6, "label": "PERMANENT_REDIRECT", "sortText": "4a.9999.PERMANENT_REDIRECT"}, {"additionalTextEdits": [{"newText": "from click.types import ParamTypeInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import ParamTypeInfoDict\n", "kind": 7, "label": "ParamTypeInfoDict", "sortText": "4a.9999.ParamTypeInfoDict"}, {"additionalTextEdits": [{"newText": "from flask.json.tag import PassDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.json.tag import PassDict\n", "kind": 7, "label": "PassDict", "sortText": "4a.9999.PassDict"}, {"additionalTextEdits": [{"newText": "from click.types import PathInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import PathInfoDict\n", "kind": 7, "label": "PathInfoDict", "sortText": "4a.9999.PathInfoDict"}, {"additionalTextEdits": [{"newText": "from requests.models import REDIRECT_STATI\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.models import REDIRECT_STATI\n", "kind": 6, "label": "REDIRECT_STATI", "sortText": "4a.9999.REDIRECT_STATI"}, {"additionalTextEdits": [{"newText": "from requests.sessions import REDIRECT_STATI\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import REDIRECT_STATI\n", "kind": 6, "label": "REDIRECT_STATI", "sortText": "4a.9999.REDIRECT_STATI"}, {"additionalTextEdits": [{"newText": "from uuid import RESERVED_MICROSOFT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from uuid import RESERVED_MICROSOFT\n", "kind": 6, "label": "RESERVED_MICROSOFT", "sortText": "4a.9999.RESERVED_MICROSOFT"}, {"additionalTextEdits": [{"newText": "from http.client import RemoteDisconnected\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.client import RemoteDisconnected\n", "kind": 7, "label": "RemoteDisconnected", "sortText": "4a.9999.RemoteDisconnected"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing import RequestAliasRedirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing import RequestAliasRedirect\n", "kind": 7, "label": "RequestAliasRedirect", "sortText": "4a.9999.RequestAliasRedirect"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.exceptions import RequestAliasRedirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.exceptions import RequestAliasRedirect\n", "kind": 7, "label": "RequestAliasRedirect", "sortText": "4a.9999.RequestAliasRedirect"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing import RequestRedirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing import RequestRedirect\n", "kind": 7, "label": "RequestRedirect", "sortText": "4a.9999.RequestRedirect"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.exceptions import RequestRedirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.exceptions import RequestRedirect\n", "kind": 7, "label": "RequestRedirect", "sortText": "4a.9999.RequestRedirect"}, {"additionalTextEdits": [{"newText": "from requests.sessions import SessionRedirectMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import SessionRedirectMixin\n", "kind": 7, "label": "SessionRedirectMixin", "sortText": "4a.9999.SessionRedirectMixin"}, {"additionalTextEdits": [{"newText": "from http.client import TEMPORARY_REDIRECT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.client import TEMPORARY_REDIRECT\n", "kind": 6, "label": "TEMPORARY_REDIRECT", "sortText": "4a.9999.TEMPORARY_REDIRECT"}, {"additionalTextEdits": [{"newText": "from flask.json.tag import TagDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.json.tag import TagDict\n", "kind": 7, "label": "TagDict", "sortText": "4a.9999.TagDict"}, {"additionalTextEdits": [{"newText": "from tempfile import TemporaryDirectory\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from tempfile import TemporaryDirectory\n", "kind": 7, "label": "TemporaryDirectory", "sortText": "4a.9999.TemporaryDirectory"}, {"additionalTextEdits": [{"newText": "from requests import TooManyRedirects\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests import TooManyRedirects\n", "kind": 7, "label": "TooManyRedirects", "sortText": "4a.9999.TooManyRedirects"}, {"additionalTextEdits": [{"newText": "from requests.exceptions import TooManyRedirects\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.exceptions import TooManyRedirects\n", "kind": 7, "label": "TooManyRedirects", "sortText": "4a.9999.TooManyRedirects"}, {"additionalTextEdits": [{"newText": "from requests.sessions import TooManyRedirects\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import TooManyRedirects\n", "kind": 6, "label": "TooManyRedirects", "sortText": "4a.9999.TooManyRedirects"}, {"additionalTextEdits": [{"newText": "from click.types import TupleInfoDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import TupleInfoDict\n", "kind": 7, "label": "TupleInfoDict", "sortText": "4a.9999.TupleInfoDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import TypeConversionDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import TypeConversionDict\n", "kind": 7, "label": "TypeConversionDict", "sortText": "4a.9999.TypeConversionDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import TypeConversionDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import TypeConversionDict\n", "kind": 7, "label": "TypeConversionDict", "sortText": "4a.9999.TypeConversionDict"}, {"additionalTextEdits": [{"newText": "from typing import TypedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import TypedDict\n", "kind": 6, "label": "TypedDict", "sortText": "4a.9999.TypedDict"}, {"additionalTextEdits": [{"newText": "from typing_extensions import TypedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing_extensions import TypedDict\n", "kind": 6, "label": "TypedDict", "sortText": "4a.9999.TypedDict"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import UpdateDictMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import UpdateDictMixin\n", "kind": 7, "label": "UpdateDictMixin", "sortText": "4a.9999.UpdateDictMixin"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.mixins import UpdateDictMixin\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.mixins import UpdateDictMixin\n", "kind": 7, "label": "UpdateDictMixin", "sortText": "4a.9999.UpdateDictMixin"}, {"additionalTextEdits": [{"newText": "from collections import UserDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import UserDict\n", "kind": 7, "label": "UserDict", "sortText": "4a.9999.UserDict"}, {"additionalTextEdits": [{"newText": "from weakref import WeakKeyDictionary\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from weakref import WeakKeyDictionary\n", "kind": 7, "label": "WeakKeyDictionary", "sortText": "4a.9999.WeakKeyDictionary"}, {"additionalTextEdits": [{"newText": "from weakref import WeakValueDictionary\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from weakref import WeakValueDictionary\n", "kind": 7, "label": "WeakValueDictionary", "sortText": "4a.9999.WeakValueDictionary"}, {"additionalTextEdits": [{"newText": "from codecs import _BufferedIncrementalDecoder\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from codecs import _BufferedIncrementalDecoder\n", "kind": 7, "label": "_BufferedIncrementalDecoder", "sortText": "4a.9999._BufferedIncrementalDecoder"}, {"additionalTextEdits": [{"newText": "from enum import _EnumDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from enum import _EnumDict\n", "kind": 7, "label": "_EnumDict", "sortText": "4a.9999._EnumDict"}, {"additionalTextEdits": [{"newText": "from inspect import _GetMembersPredicate\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from inspect import _GetMembersPredicate\n", "kind": 6, "label": "_GetMembersPredicate", "sortText": "4a.9999._GetMembersPredicate"}, {"additionalTextEdits": [{"newText": "from inspect import _GetMembersPredicateTypeGuard\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from inspect import _GetMembersPredicateTypeGuard\n", "kind": 6, "label": "_GetMembersPredicateTypeGuard", "sortText": "4a.9999._GetMembersPredicateTypeGuard"}, {"additionalTextEdits": [{"newText": "from inspect import _GetMembersPredicateTypeIs\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from inspect import _GetMembersPredicateTypeIs\n", "kind": 6, "label": "_GetMembersPredicateTypeIs", "sortText": "4a.9999._GetMembersPredicateTypeIs"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import _ImmutableOrderedMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import _ImmutableOrderedMultiDict\n", "kind": 7, "label": "_ImmutableOrderedMultiDict", "sortText": "4a.9999._ImmutableOrderedMultiDict"}, {"additionalTextEdits": [{"newText": "from collections import _OrderedDictItemsView\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import _OrderedDictItemsView\n", "kind": 7, "label": "_OrderedDictItemsView", "sortText": "4a.9999._OrderedDictItemsView"}, {"additionalTextEdits": [{"newText": "from collections import _OrderedDictKeysView\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import _OrderedDictKeysView\n", "kind": 7, "label": "_OrderedDictKeysView", "sortText": "4a.9999._OrderedDictKeysView"}, {"additionalTextEdits": [{"newText": "from collections import _OrderedDictValuesView\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import _OrderedDictValuesView\n", "kind": 7, "label": "_OrderedDictValuesView", "sortText": "4a.9999._OrderedDictValuesView"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import _OrderedMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import _OrderedMultiDict\n", "kind": 7, "label": "_OrderedMultiDict", "sortText": "4a.9999._OrderedMultiDict"}, {"additionalTextEdits": [{"newText": "from ssl import _PeerCertRetDictType\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import _PeerCertRetDictType\n", "kind": 6, "label": "_PeerCertRetDictType", "sortText": "4a.9999._PeerCertRetDictType"}, {"additionalTextEdits": [{"newText": "from itertools import _Predicate\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itertools import _Predicate\n", "kind": 6, "label": "_Predicate", "sortText": "4a.9999._Predicate"}, {"additionalTextEdits": [{"newText": "from contextlib import _RedirectStream\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextlib import _RedirectStream\n", "kind": 7, "label": "_RedirectStream", "sortText": "4a.9999._RedirectStream"}, {"additionalTextEdits": [{"newText": "from click.shell_completion import _SourceVarsDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.shell_completion import _SourceVarsDict\n", "kind": 7, "label": "_SourceVarsDict", "sortText": "4a.9999._SourceVarsDict"}, {"additionalTextEdits": [{"newText": "from contextlib import _SupportsRedirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextlib import _SupportsRedirect\n", "kind": 7, "label": "_SupportsRedirect", "sortText": "4a.9999._SupportsRedirect"}, {"additionalTextEdits": [{"newText": "from contextlib import _SupportsRedirectT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextlib import _SupportsRedirectT\n", "kind": 6, "label": "_SupportsRedirectT", "sortText": "4a.9999._SupportsRedirectT"}, {"additionalTextEdits": [{"newText": "from werkzeug.test import _TAnyMultiDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.test import _TAnyMultiDict\n", "kind": 6, "label": "_TAnyMultiDict", "sortText": "4a.9999._TAnyMultiDict"}, {"additionalTextEdits": [{"newText": "from urllib3.util.ssl_ import _TYPE_PEER_CERT_RET_DICT\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.ssl_ import _TYPE_PEER_CERT_RET_DICT\n", "kind": 7, "label": "_TYPE_PEER_CERT_RET_DICT", "sortText": "4a.9999._TYPE_PEER_CERT_RET_DICT"}, {"additionalTextEdits": [{"newText": "from typing import _TypedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import _TypedDict\n", "kind": 7, "label": "_TypedDict", "sortText": "4a.9999._TypedDict"}, {"additionalTextEdits": [{"newText": "from typing_extensions import _TypedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing_extensions import _TypedDict\n", "kind": 7, "label": "_TypedDict", "sortText": "4a.9999._TypedDict"}, {"additionalTextEdits": [{"newText": "from colorama.ansitowin32 import _WinTermCallDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from colorama.ansitowin32 import _WinTermCallDict\n", "kind": 6, "label": "_WinTermCallDict", "sortText": "4a.9999._WinTermCallDict"}, {"additionalTextEdits": [{"newText": "from urllib3.connection import _WrappedAndVerifiedSocket\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.connection import _WrappedAndVerifiedSocket\n", "kind": 7, "label": "_WrappedAndVerifiedSocket", "sortText": "4a.9999._WrappedAndVerifiedSocket"}, {"additionalTextEdits": [{"newText": "from abc import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from abc import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from ast import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ast import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.base_events import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.base_events import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.coroutines import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.coroutines import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.events import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.events import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.futures import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.futures import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.locks import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.locks import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.protocols import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.protocols import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.queues import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.queues import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.runners import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.runners import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.streams import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.streams import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.subprocess import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.subprocess import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.taskgroups import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.taskgroups import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.tasks import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.tasks import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.threads import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.threads import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.timeouts import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.timeouts import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.transports import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.transports import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from asyncio.unix_events import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from asyncio.unix_events import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from atexit import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from atexit import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from base64 import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from base64 import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from binascii import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from binascii import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from blinker import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from blinker import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from blinker.base import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from blinker.base import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.core import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.core import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.decorators import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.decorators import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.formatting import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.formatting import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.globals import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.globals import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.parser import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.parser import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.shell_completion import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.shell_completion import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.termui import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.termui import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.testing import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.testing import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.types import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.types import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click.utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click.utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from code import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from code import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from codecs import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from codecs import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from collections import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from collections.abc import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections.abc import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from colorama import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from colorama import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from colorama.ansitowin32 import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from colorama.ansitowin32 import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from contextlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from contextvars import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextvars import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from copy import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from copy import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from dataclasses import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from dataclasses import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from datetime import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from datetime import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from decimal import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from decimal import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from difflib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from difflib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from email.utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from email.utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from enum import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from enum import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from errno import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from errno import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.app import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.app import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.blueprints import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.blueprints import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.cli import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.cli import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.config import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.config import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.ctx import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.ctx import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.debughelpers import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.debughelpers import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.globals import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.globals import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.helpers import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.helpers import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.json import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.json import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.json.provider import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.json.provider import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.json.tag import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.json.tag import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.logging import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.logging import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.sansio.app import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.sansio.app import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.sansio.blueprints import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.sansio.blueprints import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.sansio.scaffold import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.sansio.scaffold import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.sessions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.sessions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.signals import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.signals import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.templating import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.templating import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.testing import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.testing import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.typing import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.typing import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from flask.wrappers import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.wrappers import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from fnmatch import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from fnmatch import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from functools import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from functools import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from genericpath import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from genericpath import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from getpass import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from getpass import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from gettext import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from gettext import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from glob import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from glob import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from hashlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from hashlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from hmac import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from hmac import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from html import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from html import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from http import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from http.client import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.client import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from http.cookiejar import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.cookiejar import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from http.cookies import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.cookies import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from http.server import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from http.server import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from idna import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from idna import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from idna.core import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from idna.core import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib.abc import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib.abc import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib.machinery import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib.machinery import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib.metadata import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib.metadata import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib.util import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib.util import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from inspect import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from inspect import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from io import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from io import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itertools import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itertools import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous.encoding import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous.encoding import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous.exc import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous.exc import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous.serializer import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous.serializer import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous.signer import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous.signer import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous.timed import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous.timed import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from itsdangerous.url_safe import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from itsdangerous.url_safe import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2 import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2 import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.async_utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.async_utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.bccache import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.bccache import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.compiler import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.compiler import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.debug import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.debug import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.environment import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.environment import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.lexer import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.lexer import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.loaders import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.loaders import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.nodes import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.nodes import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.parser import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.parser import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.runtime import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.runtime import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from jinja2.utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from json import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from json import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from json.decoder import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from json.decoder import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from json.encoder import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from json.encoder import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from logging import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from logging import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from markupsafe import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from markupsafe import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from marshal import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from marshal import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from math import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from math import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from mimetypes import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from mimetypes import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from numbers import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from numbers import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from operator import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from operator import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from os import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from os import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from os.path import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from os.path import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from pathlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from pathlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from pickle import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from pickle import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from pkgutil import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from pkgutil import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from platform import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from platform import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from posixpath import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from posixpath import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from pprint import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from pprint import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from queue import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from queue import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from random import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from random import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from re import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from re import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from readline import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from readline import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.adapters import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.adapters import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.api import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.api import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.auth import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.auth import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.compat import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.compat import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.cookies import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.cookies import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.hooks import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.hooks import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.models import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.models import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.packages import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.packages import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.sessions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.status_codes import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.status_codes import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.structures import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.structures import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from rlcompleter import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from rlcompleter import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from secrets import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from secrets import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from selectors import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from selectors import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from shlex import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from shlex import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from shutil import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from shutil import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from socket import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from socket import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from socketserver import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from socketserver import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from socks import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from socks import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from ssl import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from ssl import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from stat import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from stat import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from string import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from string import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from subprocess import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from subprocess import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from sys import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from sys import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from tempfile import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from tempfile import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from termios import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from termios import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from threading import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from threading import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from time import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from time import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from traceback import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from traceback import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from tty import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from tty import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from types import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from types import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from typing import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from typing_extensions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing_extensions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from unicodedata import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from unicodedata import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib.parse import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib.parse import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3 import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3 import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.connection import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.connection import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.connectionpool import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.connectionpool import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.contrib.socks import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.contrib.socks import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.fields import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.fields import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.filepost import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.filepost import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.poolmanager import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.poolmanager import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.response import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.response import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.connection import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.connection import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.proxy import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.proxy import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.request import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.request import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.response import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.response import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.retry import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.retry import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.ssl_ import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.ssl_ import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.timeout import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.timeout import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.url import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.url import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3.util.util import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3.util.util import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from uuid import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from uuid import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from warnings import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from warnings import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from weakref import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from weakref import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from webbrowser import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from webbrowser import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.accept import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.accept import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.auth import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.auth import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.cache_control import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.cache_control import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.csp import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.csp import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.etag import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.etag import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.file_storage import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.file_storage import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.headers import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.headers import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.mixins import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.mixins import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.range import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.range import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.datastructures.structures import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.datastructures.structures import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.debug import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.debug import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.debug.tbtools import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.debug.tbtools import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.formparser import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.formparser import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.http import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.http import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.local import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.local import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.middleware.shared_data import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.middleware.shared_data import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.converters import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.converters import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.exceptions import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.exceptions import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.map import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.map import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.matcher import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.matcher import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.routing.rules import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.routing.rules import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.sansio import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.sansio import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.sansio.http import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.sansio.http import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.sansio.multipart import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.sansio.multipart import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.sansio.request import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.sansio.request import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.sansio.response import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.sansio.response import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.sansio.utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.sansio.utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.security import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.security import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.serving import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.serving import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.test import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.test import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.urls import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.urls import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.user_agent import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.user_agent import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.wrappers import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.wrappers import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.wrappers.request import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.wrappers.request import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.wrappers.response import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.wrappers.response import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug.wsgi import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.wsgi import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from wsgiref.types import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from wsgiref.types import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from zipfile import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from zipfile import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from zipimport import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from zipimport import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from zlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from zlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4a.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from idna.core import _bidi_rtl_categories\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from idna.core import _bidi_rtl_categories\n", "kind": 6, "label": "_bidi_rtl_categories", "sortText": "4a.9999._bidi_rtl_categories"}, {"additionalTextEdits": [{"newText": "from jinja2.runtime import _dict_method_all\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from jinja2.runtime import _dict_method_all\n", "kind": 3, "label": "_dict_method_all", "sortText": "4a.9999._dict_method_all"}, {"additionalTextEdits": [{"newText": "from importlib.util import _incompatible_extension_module_restrictions\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib.util import _incompatible_extension_module_restrictions\n", "kind": 7, "label": "_incompatible_extension_module_restrictions", "sortText": "4a.9999._incompatible_extension_module_restrictions"}, {"additionalTextEdits": [{"newText": "from collections import _odict_items\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import _odict_items\n", "kind": 7, "label": "_odict_items", "sortText": "4a.9999._odict_items"}, {"additionalTextEdits": [{"newText": "from collections import _odict_keys\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import _odict_keys\n", "kind": 7, "label": "_odict_keys", "sortText": "4a.9999._odict_keys"}, {"additionalTextEdits": [{"newText": "from collections import _odict_values\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import _odict_values\n", "kind": 7, "label": "_odict_values", "sortText": "4a.9999._odict_values"}, {"additionalTextEdits": [{"newText": "from flask.helpers import _wz_redirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.helpers import _wz_redirect\n", "kind": 3, "label": "_wz_redirect", "sortText": "4a.9999._wz_redirect"}, {"additionalTextEdits": [{"newText": "from flask.sansio.app import _wz_redirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.sansio.app import _wz_redirect\n", "kind": 3, "label": "_wz_redirect", "sortText": "4a.9999._wz_redirect"}, {"additionalTextEdits": [{"newText": "from requests.utils import add_dict_to_cookiejar\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import add_dict_to_cookiejar\n", "kind": 3, "label": "add_dict_to_cookiejar", "sortText": "4a.9999.add_dict_to_cookiejar"}, {"additionalTextEdits": [{"newText": "from werkzeug.utils import append_slash_redirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.utils import append_slash_redirect\n", "kind": 3, "label": "append_slash_redirect", "sortText": "4a.9999.append_slash_redirect"}, {"additionalTextEdits": [{"newText": "from dataclasses import asdict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from dataclasses import asdict\n", "kind": 3, "label": "asdict", "sortText": "4a.9999.asdict"}, {"additionalTextEdits": [{"newText": "from flask.debughelpers import attach_enctype_error_multidict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.debughelpers import attach_enctype_error_multidict\n", "kind": 3, "label": "attach_enctype_error_multidict", "sortText": "4a.9999.attach_enctype_error_multidict"}, {"additionalTextEdits": [{"newText": "from unicodedata import bidirectional\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from unicodedata import bidirectional\n", "kind": 3, "label": "bidirectional", "sortText": "4a.9999.bidirectional"}, {"additionalTextEdits": [{"newText": "from requests.cookies import cookiejar_from_dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.cookies import cookiejar_from_dict\n", "kind": 3, "label": "cookiejar_from_dict", "sortText": "4a.9999.cookiejar_from_dict"}, {"additionalTextEdits": [{"newText": "from requests.models import cookiejar_from_dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.models import cookiejar_from_dict\n", "kind": 6, "label": "cookiejar_from_dict", "sortText": "4a.9999.cookiejar_from_dict"}, {"additionalTextEdits": [{"newText": "from requests.sessions import cookiejar_from_dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.sessions import cookiejar_from_dict\n", "kind": 6, "label": "cookiejar_from_dict", "sortText": "4a.9999.cookiejar_from_dict"}, {"additionalTextEdits": [{"newText": "from requests.utils import cookiejar_from_dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import cookiejar_from_dict\n", "kind": 6, "label": "cookiejar_from_dict", "sortText": "4a.9999.cookiejar_from_dict"}, {"additionalTextEdits": [{"newText": "from sys import deactivate_stack_trampoline\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from sys import deactivate_stack_trampoline\n", "kind": 3, "label": "deactivate_stack_trampoline", "sortText": "4a.9999.deactivate_stack_trampoline"}, {"additionalTextEdits": [{"newText": "from collections import defaultdict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from collections import defaultdict\n", "kind": 7, "label": "defaultdict", "sortText": "4a.9999.defaultdict"}, {"additionalTextEdits": [{"newText": "from requests.utils import dict_from_cookiejar\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import dict_from_cookiejar\n", "kind": 3, "label": "dict_from_cookiejar", "sortText": "4a.9999.dict_from_cookiejar"}, {"additionalTextEdits": [{"newText": "from requests.utils import dict_to_sequence\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import dict_to_sequence\n", "kind": 3, "label": "dict_to_sequence", "sortText": "4a.9999.dict_to_sequence"}, {"additionalTextEdits": [{"newText": "from requests.utils import get_encodings_from_content\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import get_encodings_from_content\n", "kind": 3, "label": "get_encodings_from_content", "sortText": "4a.9999.get_encodings_from_content"}, {"additionalTextEdits": [{"newText": "from typing import is_typeddict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import is_typeddict\n", "kind": 3, "label": "is_typeddict", "sortText": "4a.9999.is_typeddict"}, {"additionalTextEdits": [{"newText": "from requests.auth import parse_dict_header\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.auth import parse_dict_header\n", "kind": 6, "label": "parse_dict_header", "sortText": "4a.9999.parse_dict_header"}, {"additionalTextEdits": [{"newText": "from requests.utils import parse_dict_header\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.utils import parse_dict_header\n", "kind": 3, "label": "parse_dict_header", "sortText": "4a.9999.parse_dict_header"}, {"additionalTextEdits": [{"newText": "from werkzeug.http import parse_dict_header\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.http import parse_dict_header\n", "kind": 3, "label": "parse_dict_header", "sortText": "4a.9999.parse_dict_header"}, {"additionalTextEdits": [{"newText": "from flask import redirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask import redirect\n", "kind": 3, "label": "redirect", "sortText": "4a.9999.redirect"}, {"additionalTextEdits": [{"newText": "from flask.helpers import redirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.helpers import redirect\n", "kind": 3, "label": "redirect", "sortText": "4a.9999.redirect"}, {"additionalTextEdits": [{"newText": "from werkzeug.utils import redirect\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.utils import redirect\n", "kind": 3, "label": "redirect", "sortText": "4a.9999.redirect"}, {"additionalTextEdits": [{"newText": "from contextlib import redirect_stderr\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextlib import redirect_stderr\n", "kind": 7, "label": "redirect_stderr", "sortText": "4a.9999.redirect_stderr"}, {"additionalTextEdits": [{"newText": "from contextlib import redirect_stdout\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from contextlib import redirect_stdout\n", "kind": 7, "label": "redirect_stdout", "sortText": "4a.9999.redirect_stdout"}, {"additionalTextEdits": [{"newText": "from flask import send_from_directory\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask import send_from_directory\n", "kind": 3, "label": "send_from_directory", "sortText": "4a.9999.send_from_directory"}, {"additionalTextEdits": [{"newText": "from flask.helpers import send_from_directory\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from flask.helpers import send_from_directory\n", "kind": 3, "label": "send_from_directory", "sortText": "4a.9999.send_from_directory"}, {"additionalTextEdits": [{"newText": "from werkzeug.utils import send_from_directory\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug.utils import send_from_directory\n", "kind": 3, "label": "send_from_directory", "sortText": "4a.9999.send_from_directory"}, {"additionalTextEdits": [{"newText": "from functools import singledispatchmethod\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from functools import singledispatchmethod\n", "kind": 7, "label": "singledispatchmethod", "sortText": "4a.9999.singledispatchmethod"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE\n", "kind": 6, "label": "ALERT_DESCRIPTION_BAD_CERTIFICATE", "sortText": "4b.9999.ALERT_DESCRIPTION_BAD_CERTIFICATE"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE\n", "kind": 6, "label": "ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE", "sortText": "4b.9999.ALERT_DESCRIPTION_BAD_CERTIFICATE_HASH_VALUE"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE\n", "kind": 6, "label": "ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE", "sortText": "4b.9999.ALERT_DESCRIPTION_BAD_CERTIFICATE_STATUS_RESPONSE"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_EXPIRED\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_EXPIRED\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_EXPIRED", "sortText": "4b.9999.ALERT_DESCRIPTION_CERTIFICATE_EXPIRED"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_REVOKED\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_REVOKED\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_REVOKED", "sortText": "4b.9999.ALERT_DESCRIPTION_CERTIFICATE_REVOKED"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN", "sortText": "4b.9999.ALERT_DESCRIPTION_CERTIFICATE_UNKNOWN"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE\n", "kind": 6, "label": "ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE", "sortText": "4b.9999.ALERT_DESCRIPTION_CERTIFICATE_UNOBTAINABLE"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_CLOSE_NOTIFY\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_CLOSE_NOTIFY\n", "kind": 6, "label": "ALERT_DESCRIPTION_CLOSE_NOTIFY", "sortText": "4b.9999.ALERT_DESCRIPTION_CLOSE_NOTIFY"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_DECRYPT_ERROR\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_DECRYPT_ERROR\n", "kind": 6, "label": "ALERT_DESCRIPTION_DECRYPT_ERROR", "sortText": "4b.9999.ALERT_DESCRIPTION_DECRYPT_ERROR"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_INSUFFICIENT_SECURITY\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_INSUFFICIENT_SECURITY\n", "kind": 6, "label": "ALERT_DESCRIPTION_INSUFFICIENT_SECURITY", "sortText": "4b.9999.ALERT_DESCRIPTION_INSUFFICIENT_SECURITY"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_UNEXPECTED_MESSAGE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_UNEXPECTED_MESSAGE\n", "kind": 6, "label": "ALERT_DESCRIPTION_UNEXPECTED_MESSAGE", "sortText": "4b.9999.ALERT_DESCRIPTION_UNEXPECTED_MESSAGE"}, {"additionalTextEdits": [{"newText": "from _ssl import ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE\n", "kind": 6, "label": "ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE", "sortText": "4b.9999.ALERT_DESCRIPTION_UNSUPPORTED_CERTIFICATE"}, {"additionalTextEdits": [{"newText": "from urllib3._collections import HTTPHeaderDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._collections import HTTPHeaderDict\n", "kind": 7, "label": "HTTPHeaderDict", "sortText": "4b.9999.HTTPHeaderDict"}, {"additionalTextEdits": [{"newText": "from urllib3._collections import HTTPHeaderDictItemView\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._collections import HTTPHeaderDictItemView\n", "kind": 7, "label": "HTTPHeaderDictItemView", "sortText": "4b.9999.HTTPHeaderDictItemView"}, {"additionalTextEdits": [{"newText": "from _socket import IP_DEFAULT_MULTICAST_LOOP\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _socket import IP_DEFAULT_MULTICAST_LOOP\n", "kind": 6, "label": "IP_DEFAULT_MULTICAST_LOOP", "sortText": "4b.9999.IP_DEFAULT_MULTICAST_LOOP"}, {"additionalTextEdits": [{"newText": "from _socket import IP_DEFAULT_MULTICAST_TTL\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _socket import IP_DEFAULT_MULTICAST_TTL\n", "kind": 6, "label": "IP_DEFAULT_MULTICAST_TTL", "sortText": "4b.9999.IP_DEFAULT_MULTICAST_TTL"}, {"additionalTextEdits": [{"newText": "from _typeshed import IdentityFunction\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _typeshed import IdentityFunction\n", "kind": 7, "label": "IdentityFunction", "sortText": "4b.9999.IdentityFunction"}, {"additionalTextEdits": [{"newText": "from _typeshed._type_checker_internals import TypedDictFallback\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _typeshed._type_checker_internals import TypedDictFallback\n", "kind": 7, "label": "TypedDictFallback", "sortText": "4b.9999.TypedDictFallback"}, {"additionalTextEdits": [{"newText": "from werkzeug._internal import _DictAccessorProperty\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug._internal import _DictAccessorProperty\n", "kind": 7, "label": "_DictAccessorProperty", "sortText": "4b.9999._DictAccessorProperty"}, {"additionalTextEdits": [{"newText": "from _ssl import _PeerCertRetDictType\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import _PeerCertRetDictType\n", "kind": 6, "label": "_PeerCertRetDictType", "sortText": "4b.9999._PeerCertRetDictType"}, {"additionalTextEdits": [{"newText": "from __future__ import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from __future__ import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _codecs import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _codecs import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _collections_abc import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _collections_abc import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _contextvars import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _contextvars import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _frozen_importlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _frozen_importlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _frozen_importlib_external import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _frozen_importlib_external import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _hashlib import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _hashlib import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _io import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _io import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _operator import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _operator import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _pickle import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _pickle import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _random import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _random import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _socket import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _socket import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _ssl import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _ssl import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _stat import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _stat import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _thread import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _thread import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _typeshed import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _typeshed import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _typeshed._type_checker_internals import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _typeshed._type_checker_internals import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _typeshed.wsgi import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _typeshed.wsgi import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _warnings import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _warnings import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _weakref import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _weakref import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from blinker._utilities import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from blinker._utilities import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click._compat import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click._compat import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click._termui_impl import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click._termui_impl import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click._textwrap import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click._textwrap import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from click._utils import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from click._utils import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib._abc import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib._abc import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib._bootstrap import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib._bootstrap import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib._bootstrap_external import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib._bootstrap_external import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from importlib.metadata._meta import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from importlib.metadata._meta import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from markupsafe._native import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from markupsafe._native import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from markupsafe._speedups import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from markupsafe._speedups import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from requests.__version__ import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from requests.__version__ import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3._base_connection import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._base_connection import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3._collections import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._collections import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3._request_methods import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._request_methods import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from urllib3._version import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._version import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug._internal import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug._internal import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from werkzeug._reloader import __dict__\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from werkzeug._reloader import __dict__\n", "kind": 21, "label": "__dict__", "sortText": "4b.9999.__dict__"}, {"additionalTextEdits": [{"newText": "from _collections_abc import dict_items\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _collections_abc import dict_items\n", "kind": 7, "label": "dict_items", "sortText": "4b.9999.dict_items"}, {"additionalTextEdits": [{"newText": "from _collections_abc import dict_keys\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _collections_abc import dict_keys\n", "kind": 7, "label": "dict_keys", "sortText": "4b.9999.dict_keys"}, {"additionalTextEdits": [{"newText": "from _collections_abc import dict_values\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from _collections_abc import dict_values\n", "kind": 7, "label": "dict_values", "sortText": "4b.9999.dict_values"}, {"additionalTextEdits": [{"newText": "from urllib3._collections import ensure_can_construct_http_header_dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from urllib3._collections import ensure_can_construct_http_header_dict\n", "kind": 3, "label": "ensure_can_construct_http_header_dict", "sortText": "4b.9999.ensure_can_construct_http_header_dict"}, {"additionalTextEdits": [{"newText": "from typing import DefaultDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import DefaultDict\n", "kind": 6, "label": "DefaultDict", "sortText": "9.9999.DefaultDict", "tags": [1]}, {"additionalTextEdits": [{"newText": "from typing import Dict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import Dict\n", "kind": 6, "label": "Dict", "sortText": "9.9999.Dict", "tags": [1]}, {"additionalTextEdits": [{"newText": "from typing import OrderedDict\n", "range": {"end": {"character": 0, "line": 0}, "start": {"character": 0, "line": 0}}}], "detail": "from typing import OrderedDict\n", "kind": 6, "label": "OrderedDict", "sortText": "9.9999.OrderedDict", "tags": [1]}]}} -{"suite": "web", "label": "client session hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 10, "character": 17, "iteration": 1, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(variable) client: requests.sessions.Session\n```\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/sessions.pyi#L115,7)"}}} -{"suite": "web", "label": "client session hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 10, "character": 17, "iteration": 2, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(variable) client: requests.sessions.Session\n```\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/sessions.pyi#L115,7)"}}} -{"suite": "web", "label": "client session hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 10, "character": 17, "iteration": 3, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(variable) client: requests.sessions.Session\n```\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/sessions.pyi#L115,7)"}}} -{"suite": "web", "label": "client session hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 10, "character": 17, "iteration": 4, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(variable) client: requests.sessions.Session\n```\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/sessions.pyi#L115,7)"}}} -{"suite": "web", "label": "client session hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 10, "character": 17, "iteration": 5, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(variable) client: requests.sessions.Session\n```\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/sessions.pyi#L115,7)"}}} +{"suite": "web", "label": "client session hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 10, "character": 17, "iteration": 1, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(variable) client: requests.sessions.Session\n```\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/sessions.pyi#L115,7)"}}} +{"suite": "web", "label": "client session hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 10, "character": 17, "iteration": 2, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(variable) client: requests.sessions.Session\n```\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/sessions.pyi#L115,7)"}}} +{"suite": "web", "label": "client session hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 10, "character": 17, "iteration": 3, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(variable) client: requests.sessions.Session\n```\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/sessions.pyi#L115,7)"}}} +{"suite": "web", "label": "client session hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 10, "character": 17, "iteration": 4, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(variable) client: requests.sessions.Session\n```\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/sessions.pyi#L115,7)"}}} +{"suite": "web", "label": "client session hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 10, "character": 17, "iteration": 5, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(variable) client: requests.sessions.Session\n```\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/sessions.pyi#L115,7)"}}} {"suite": "web", "label": "client references", "method": "textDocument/references", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 10, "character": 17, "iteration": 1, "result": [{"range": {"end": {"character": 6, "line": 5}, "start": {"character": 0, "line": 5}}, "uri": "file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py"}, {"range": {"end": {"character": 21, "line": 10}, "start": {"character": 15, "line": 10}}, "uri": "file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py"}]} {"suite": "web", "label": "client references", "method": "textDocument/references", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 10, "character": 17, "iteration": 2, "result": [{"range": {"end": {"character": 6, "line": 5}, "start": {"character": 0, "line": 5}}, "uri": "file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py"}, {"range": {"end": {"character": 21, "line": 10}, "start": {"character": 15, "line": 10}}, "uri": "file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py"}]} {"suite": "web", "label": "client references", "method": "textDocument/references", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 10, "character": 17, "iteration": 3, "result": [{"range": {"end": {"character": 6, "line": 5}, "start": {"character": 0, "line": 5}}, "uri": "file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py"}, {"range": {"end": {"character": 21, "line": 10}, "start": {"character": 15, "line": 10}}, "uri": "file:///home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py"}]} @@ -198,8 +198,8 @@ {"suite": "web", "label": "edit response then complete (edit+completion)", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 11, "character": 55, "iteration": 3, "result": {"isIncomplete": false, "items": [{"detail": "str", "kind": 5, "label": "apparent_encoding", "sortText": "0.9999.apparent_encoding"}, {"detail": "def close(self: Response) -> None: ...", "kind": 2, "label": "close", "sortText": "0.9999.close"}, {"detail": "HTTPAdapter", "kind": 5, "label": "connection", "sortText": "0.9999.connection"}, {"detail": "bytes | Any", "kind": 5, "label": "content", "sortText": "0.9999.content"}, {"detail": "RequestsCookieJar", "kind": 5, "label": "cookies", "sortText": "0.9999.cookies"}, {"detail": "timedelta", "kind": 5, "label": "elapsed", "sortText": "0.9999.elapsed"}, {"detail": "str | None", "kind": 5, "label": "encoding", "sortText": "0.9999.encoding"}, {"detail": "CaseInsensitiveDict[str]", "kind": 5, "label": "headers", "sortText": "0.9999.headers"}, {"detail": "list[Response]", "kind": 5, "label": "history", "sortText": "0.9999.history"}, {"detail": "bool", "kind": 5, "label": "is_permanent_redirect", "sortText": "0.9999.is_permanent_redirect"}, {"detail": "bool", "kind": 5, "label": "is_redirect", "sortText": "0.9999.is_redirect"}, {"detail": "def iter_content(\n self : Response,\n chunk_size : int | None = 1,\n decode_unicode: bool = False\n) -> Iterator[Any]: ...", "kind": 2, "label": "iter_content", "sortText": "0.9999.iter_content"}, {"detail": "def iter_lines(\n self : Response,\n chunk_size : int | None = 512,\n decode_unicode: bool = False,\n delimiter : bytes | str | None = None\n) -> Iterator[Any]: ...", "kind": 2, "label": "iter_lines", "sortText": "0.9999.iter_lines"}, {"detail": "def json(\n self : Response,\n *,\n cls : type[JSONDecoder] | None = ...,\n object_hook : ((dict[Any, Any]) -> Any) | None = ...,\n parse_float : ((str) -> Any) | None = ...,\n parse_int : ((str) -> Any) | None = ...,\n parse_constant : ((str) -> Any) | None = ...,\n object_pairs_hook: ((list[tuple[Any, Any]]) -> Any) | None = ...,\n **kwds : Any\n) -> Any: ...", "kind": 2, "label": "json", "sortText": "0.9999.json"}, {"detail": "dict[Any, Any]", "kind": 5, "label": "links", "sortText": "0.9999.links"}, {"detail": "PreparedRequest | None", "kind": 5, "label": "next", "sortText": "0.9999.next"}, {"detail": "bool", "kind": 5, "label": "ok", "sortText": "0.9999.ok"}, {"detail": "def raise_for_status(self: Response) -> None: ...", "kind": 2, "label": "raise_for_status", "sortText": "0.9999.raise_for_status"}, {"detail": "HTTPResponse | Any", "kind": 5, "label": "raw", "sortText": "0.9999.raw"}, {"detail": "str", "kind": 5, "label": "reason", "sortText": "0.9999.reason"}, {"detail": "PreparedRequest", "kind": 5, "label": "request", "sortText": "0.9999.request"}, {"detail": "int", "kind": 5, "label": "status_code", "sortText": "0.9999.status_code"}, {"detail": "str", "kind": 5, "label": "text", "sortText": "0.9999.text"}, {"detail": "str", "kind": 5, "label": "url", "sortText": "0.9999.url"}, {"detail": "bytes | None", "kind": 5, "label": "_content", "sortText": "2.9999._content"}, {"detail": "Any", "kind": 5, "label": "__attrs__", "sortText": "3.9999.__attrs__"}, {"detail": "def __bool__(self: Response) -> bool: ...", "kind": 2, "label": "__bool__", "sortText": "3.9999.__bool__"}, {"detail": "def __enter__(self: Response) -> Response: ...", "kind": 2, "label": "__enter__", "sortText": "3.9999.__enter__"}, {"detail": "def __exit__(\n self: Response,\n *args: object\n) -> None: ...", "kind": 2, "label": "__exit__", "sortText": "3.9999.__exit__"}, {"detail": "def __init__(self: Response) -> None: ...", "kind": 2, "label": "__init__", "sortText": "3.9999.__init__"}, {"detail": "def __iter__(self: Response) -> Iterator[bytes]: ...", "kind": 2, "label": "__iter__", "sortText": "3.9999.__iter__"}, {"detail": "def __nonzero__(self: Response) -> bool: ...", "kind": 2, "label": "__nonzero__", "sortText": "3.9999.__nonzero__"}]}} {"suite": "web", "label": "edit response then complete (edit+completion)", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 11, "character": 55, "iteration": 4, "result": {"isIncomplete": false, "items": [{"detail": "str", "kind": 5, "label": "apparent_encoding", "sortText": "0.9999.apparent_encoding"}, {"detail": "def close(self: Response) -> None: ...", "kind": 2, "label": "close", "sortText": "0.9999.close"}, {"detail": "HTTPAdapter", "kind": 5, "label": "connection", "sortText": "0.9999.connection"}, {"detail": "bytes | Any", "kind": 5, "label": "content", "sortText": "0.9999.content"}, {"detail": "RequestsCookieJar", "kind": 5, "label": "cookies", "sortText": "0.9999.cookies"}, {"detail": "timedelta", "kind": 5, "label": "elapsed", "sortText": "0.9999.elapsed"}, {"detail": "str | None", "kind": 5, "label": "encoding", "sortText": "0.9999.encoding"}, {"detail": "CaseInsensitiveDict[str]", "kind": 5, "label": "headers", "sortText": "0.9999.headers"}, {"detail": "list[Response]", "kind": 5, "label": "history", "sortText": "0.9999.history"}, {"detail": "bool", "kind": 5, "label": "is_permanent_redirect", "sortText": "0.9999.is_permanent_redirect"}, {"detail": "bool", "kind": 5, "label": "is_redirect", "sortText": "0.9999.is_redirect"}, {"detail": "def iter_content(\n self : Response,\n chunk_size : int | None = 1,\n decode_unicode: bool = False\n) -> Iterator[Any]: ...", "kind": 2, "label": "iter_content", "sortText": "0.9999.iter_content"}, {"detail": "def iter_lines(\n self : Response,\n chunk_size : int | None = 512,\n decode_unicode: bool = False,\n delimiter : bytes | str | None = None\n) -> Iterator[Any]: ...", "kind": 2, "label": "iter_lines", "sortText": "0.9999.iter_lines"}, {"detail": "def json(\n self : Response,\n *,\n cls : type[JSONDecoder] | None = ...,\n object_hook : ((dict[Any, Any]) -> Any) | None = ...,\n parse_float : ((str) -> Any) | None = ...,\n parse_int : ((str) -> Any) | None = ...,\n parse_constant : ((str) -> Any) | None = ...,\n object_pairs_hook: ((list[tuple[Any, Any]]) -> Any) | None = ...,\n **kwds : Any\n) -> Any: ...", "kind": 2, "label": "json", "sortText": "0.9999.json"}, {"detail": "dict[Any, Any]", "kind": 5, "label": "links", "sortText": "0.9999.links"}, {"detail": "PreparedRequest | None", "kind": 5, "label": "next", "sortText": "0.9999.next"}, {"detail": "bool", "kind": 5, "label": "ok", "sortText": "0.9999.ok"}, {"detail": "def raise_for_status(self: Response) -> None: ...", "kind": 2, "label": "raise_for_status", "sortText": "0.9999.raise_for_status"}, {"detail": "HTTPResponse | Any", "kind": 5, "label": "raw", "sortText": "0.9999.raw"}, {"detail": "str", "kind": 5, "label": "reason", "sortText": "0.9999.reason"}, {"detail": "PreparedRequest", "kind": 5, "label": "request", "sortText": "0.9999.request"}, {"detail": "int", "kind": 5, "label": "status_code", "sortText": "0.9999.status_code"}, {"detail": "str", "kind": 5, "label": "text", "sortText": "0.9999.text"}, {"detail": "str", "kind": 5, "label": "url", "sortText": "0.9999.url"}, {"detail": "bytes | None", "kind": 5, "label": "_content", "sortText": "2.9999._content"}, {"detail": "Any", "kind": 5, "label": "__attrs__", "sortText": "3.9999.__attrs__"}, {"detail": "def __bool__(self: Response) -> bool: ...", "kind": 2, "label": "__bool__", "sortText": "3.9999.__bool__"}, {"detail": "def __enter__(self: Response) -> Response: ...", "kind": 2, "label": "__enter__", "sortText": "3.9999.__enter__"}, {"detail": "def __exit__(\n self: Response,\n *args: object\n) -> None: ...", "kind": 2, "label": "__exit__", "sortText": "3.9999.__exit__"}, {"detail": "def __init__(self: Response) -> None: ...", "kind": 2, "label": "__init__", "sortText": "3.9999.__init__"}, {"detail": "def __iter__(self: Response) -> Iterator[bytes]: ...", "kind": 2, "label": "__iter__", "sortText": "3.9999.__iter__"}, {"detail": "def __nonzero__(self: Response) -> bool: ...", "kind": 2, "label": "__nonzero__", "sortText": "3.9999.__nonzero__"}]}} {"suite": "web", "label": "edit response then complete (edit+completion)", "method": "textDocument/completion", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 11, "character": 55, "iteration": 5, "result": {"isIncomplete": false, "items": [{"detail": "str", "kind": 5, "label": "apparent_encoding", "sortText": "0.9999.apparent_encoding"}, {"detail": "def close(self: Response) -> None: ...", "kind": 2, "label": "close", "sortText": "0.9999.close"}, {"detail": "HTTPAdapter", "kind": 5, "label": "connection", "sortText": "0.9999.connection"}, {"detail": "bytes | Any", "kind": 5, "label": "content", "sortText": "0.9999.content"}, {"detail": "RequestsCookieJar", "kind": 5, "label": "cookies", "sortText": "0.9999.cookies"}, {"detail": "timedelta", "kind": 5, "label": "elapsed", "sortText": "0.9999.elapsed"}, {"detail": "str | None", "kind": 5, "label": "encoding", "sortText": "0.9999.encoding"}, {"detail": "CaseInsensitiveDict[str]", "kind": 5, "label": "headers", "sortText": "0.9999.headers"}, {"detail": "list[Response]", "kind": 5, "label": "history", "sortText": "0.9999.history"}, {"detail": "bool", "kind": 5, "label": "is_permanent_redirect", "sortText": "0.9999.is_permanent_redirect"}, {"detail": "bool", "kind": 5, "label": "is_redirect", "sortText": "0.9999.is_redirect"}, {"detail": "def iter_content(\n self : Response,\n chunk_size : int | None = 1,\n decode_unicode: bool = False\n) -> Iterator[Any]: ...", "kind": 2, "label": "iter_content", "sortText": "0.9999.iter_content"}, {"detail": "def iter_lines(\n self : Response,\n chunk_size : int | None = 512,\n decode_unicode: bool = False,\n delimiter : bytes | str | None = None\n) -> Iterator[Any]: ...", "kind": 2, "label": "iter_lines", "sortText": "0.9999.iter_lines"}, {"detail": "def json(\n self : Response,\n *,\n cls : type[JSONDecoder] | None = ...,\n object_hook : ((dict[Any, Any]) -> Any) | None = ...,\n parse_float : ((str) -> Any) | None = ...,\n parse_int : ((str) -> Any) | None = ...,\n parse_constant : ((str) -> Any) | None = ...,\n object_pairs_hook: ((list[tuple[Any, Any]]) -> Any) | None = ...,\n **kwds : Any\n) -> Any: ...", "kind": 2, "label": "json", "sortText": "0.9999.json"}, {"detail": "dict[Any, Any]", "kind": 5, "label": "links", "sortText": "0.9999.links"}, {"detail": "PreparedRequest | None", "kind": 5, "label": "next", "sortText": "0.9999.next"}, {"detail": "bool", "kind": 5, "label": "ok", "sortText": "0.9999.ok"}, {"detail": "def raise_for_status(self: Response) -> None: ...", "kind": 2, "label": "raise_for_status", "sortText": "0.9999.raise_for_status"}, {"detail": "HTTPResponse | Any", "kind": 5, "label": "raw", "sortText": "0.9999.raw"}, {"detail": "str", "kind": 5, "label": "reason", "sortText": "0.9999.reason"}, {"detail": "PreparedRequest", "kind": 5, "label": "request", "sortText": "0.9999.request"}, {"detail": "int", "kind": 5, "label": "status_code", "sortText": "0.9999.status_code"}, {"detail": "str", "kind": 5, "label": "text", "sortText": "0.9999.text"}, {"detail": "str", "kind": 5, "label": "url", "sortText": "0.9999.url"}, {"detail": "bytes | None", "kind": 5, "label": "_content", "sortText": "2.9999._content"}, {"detail": "Any", "kind": 5, "label": "__attrs__", "sortText": "3.9999.__attrs__"}, {"detail": "def __bool__(self: Response) -> bool: ...", "kind": 2, "label": "__bool__", "sortText": "3.9999.__bool__"}, {"detail": "def __enter__(self: Response) -> Response: ...", "kind": 2, "label": "__enter__", "sortText": "3.9999.__enter__"}, {"detail": "def __exit__(\n self: Response,\n *args: object\n) -> None: ...", "kind": 2, "label": "__exit__", "sortText": "3.9999.__exit__"}, {"detail": "def __init__(self: Response) -> None: ...", "kind": 2, "label": "__init__", "sortText": "3.9999.__init__"}, {"detail": "def __iter__(self: Response) -> Iterator[bytes]: ...", "kind": 2, "label": "__iter__", "sortText": "3.9999.__iter__"}, {"detail": "def __nonzero__(self: Response) -> bool: ...", "kind": 2, "label": "__nonzero__", "sortText": "3.9999.__nonzero__"}]}} -{"suite": "web", "label": "edit response then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 11, "character": 20, "iteration": 1, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) get: def get(\n self : Session,\n url : bytes | str,\n *,\n params : Iterable[tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None]] | SupportsItems[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | bytes | str | tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | None = ...,\n data : Iterable[bytes] | Mapping[Any, Any] | SupportsRead[bytes | str] | bytes | list[tuple[Any, Any]] | str | tuple[tuple[Any, Any], ...] | None = ...,\n headers : Mapping[str, bytes | str | None] | None = ...,\n cookies : MutableMapping[str, str] | RequestsCookieJar | None = ...,\n files : Iterable[tuple[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]]] | Mapping[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]] | None = ...,\n auth : ((PreparedRequest) -> PreparedRequest) | AuthBase | tuple[str, str] | None = ...,\n timeout : float | tuple[float | None, float | None] | None = ...,\n allow_redirects: bool = ...,\n proxies : MutableMapping[str, str] | None = ...,\n hooks : Mapping[str, ((Response) -> Any) | Iterable[(Response) -> Any]] | None = ...,\n stream : bool | None = ...,\n verify : bool | str | None = ...,\n cert : str | tuple[str, str] | None = ...,\n json : Any | None = None\n) -> Response: ...\n```\n---\nSends a GET request. Returns `Response` object. \n \n:param url: URL for the new `Request` object. \n:param params: (optional) Dictionary, list of tuples or bytes to send \nin the query string for the `Request`. \n:param \\*\\*kwargs: Optional arguments that ``request`` takes. \n:rtype: requests.Response\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/sessions.pyi#L115,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1902,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [float](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L735,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L566,7) | [SupportsItems](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/_typeshed/__init__.pyi#L164,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L862,7) | [SupportsRead](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/_typeshed/__init__.pyi#L269,7) | [list](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3464,7) | [MutableMapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L885,7) | [RequestsCookieJar](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/cookies.pyi#L38,7) | [PreparedRequest](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/models.pyi#L87,7) | [AuthBase](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/auth.pyi#L14,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [Response](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/models.pyi#L117,7)"}}} -{"suite": "web", "label": "edit response then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 11, "character": 20, "iteration": 2, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) get: def get(\n self : Session,\n url : bytes | str,\n *,\n params : Iterable[tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None]] | SupportsItems[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | bytes | str | tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | None = ...,\n data : Iterable[bytes] | Mapping[Any, Any] | SupportsRead[bytes | str] | bytes | list[tuple[Any, Any]] | str | tuple[tuple[Any, Any], ...] | None = ...,\n headers : Mapping[str, bytes | str | None] | None = ...,\n cookies : MutableMapping[str, str] | RequestsCookieJar | None = ...,\n files : Iterable[tuple[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]]] | Mapping[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]] | None = ...,\n auth : ((PreparedRequest) -> PreparedRequest) | AuthBase | tuple[str, str] | None = ...,\n timeout : float | tuple[float | None, float | None] | None = ...,\n allow_redirects: bool = ...,\n proxies : MutableMapping[str, str] | None = ...,\n hooks : Mapping[str, ((Response) -> Any) | Iterable[(Response) -> Any]] | None = ...,\n stream : bool | None = ...,\n verify : bool | str | None = ...,\n cert : str | tuple[str, str] | None = ...,\n json : Any | None = None\n) -> Response: ...\n```\n---\nSends a GET request. Returns `Response` object. \n \n:param url: URL for the new `Request` object. \n:param params: (optional) Dictionary, list of tuples or bytes to send \nin the query string for the `Request`. \n:param \\*\\*kwargs: Optional arguments that ``request`` takes. \n:rtype: requests.Response\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/sessions.pyi#L115,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1902,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [float](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L735,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L566,7) | [SupportsItems](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/_typeshed/__init__.pyi#L164,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L862,7) | [SupportsRead](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/_typeshed/__init__.pyi#L269,7) | [list](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3464,7) | [MutableMapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L885,7) | [RequestsCookieJar](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/cookies.pyi#L38,7) | [PreparedRequest](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/models.pyi#L87,7) | [AuthBase](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/auth.pyi#L14,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [Response](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/models.pyi#L117,7)"}}} -{"suite": "web", "label": "edit response then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 11, "character": 20, "iteration": 3, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) get: def get(\n self : Session,\n url : bytes | str,\n *,\n params : Iterable[tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None]] | SupportsItems[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | bytes | str | tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | None = ...,\n data : Iterable[bytes] | Mapping[Any, Any] | SupportsRead[bytes | str] | bytes | list[tuple[Any, Any]] | str | tuple[tuple[Any, Any], ...] | None = ...,\n headers : Mapping[str, bytes | str | None] | None = ...,\n cookies : MutableMapping[str, str] | RequestsCookieJar | None = ...,\n files : Iterable[tuple[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]]] | Mapping[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]] | None = ...,\n auth : ((PreparedRequest) -> PreparedRequest) | AuthBase | tuple[str, str] | None = ...,\n timeout : float | tuple[float | None, float | None] | None = ...,\n allow_redirects: bool = ...,\n proxies : MutableMapping[str, str] | None = ...,\n hooks : Mapping[str, ((Response) -> Any) | Iterable[(Response) -> Any]] | None = ...,\n stream : bool | None = ...,\n verify : bool | str | None = ...,\n cert : str | tuple[str, str] | None = ...,\n json : Any | None = None\n) -> Response: ...\n```\n---\nSends a GET request. Returns `Response` object. \n \n:param url: URL for the new `Request` object. \n:param params: (optional) Dictionary, list of tuples or bytes to send \nin the query string for the `Request`. \n:param \\*\\*kwargs: Optional arguments that ``request`` takes. \n:rtype: requests.Response\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/sessions.pyi#L115,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1902,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [float](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L735,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L566,7) | [SupportsItems](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/_typeshed/__init__.pyi#L164,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L862,7) | [SupportsRead](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/_typeshed/__init__.pyi#L269,7) | [list](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3464,7) | [MutableMapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L885,7) | [RequestsCookieJar](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/cookies.pyi#L38,7) | [PreparedRequest](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/models.pyi#L87,7) | [AuthBase](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/auth.pyi#L14,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [Response](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/models.pyi#L117,7)"}}} -{"suite": "web", "label": "edit response then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 11, "character": 20, "iteration": 4, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) get: def get(\n self : Session,\n url : bytes | str,\n *,\n params : Iterable[tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None]] | SupportsItems[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | bytes | str | tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | None = ...,\n data : Iterable[bytes] | Mapping[Any, Any] | SupportsRead[bytes | str] | bytes | list[tuple[Any, Any]] | str | tuple[tuple[Any, Any], ...] | None = ...,\n headers : Mapping[str, bytes | str | None] | None = ...,\n cookies : MutableMapping[str, str] | RequestsCookieJar | None = ...,\n files : Iterable[tuple[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]]] | Mapping[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]] | None = ...,\n auth : ((PreparedRequest) -> PreparedRequest) | AuthBase | tuple[str, str] | None = ...,\n timeout : float | tuple[float | None, float | None] | None = ...,\n allow_redirects: bool = ...,\n proxies : MutableMapping[str, str] | None = ...,\n hooks : Mapping[str, ((Response) -> Any) | Iterable[(Response) -> Any]] | None = ...,\n stream : bool | None = ...,\n verify : bool | str | None = ...,\n cert : str | tuple[str, str] | None = ...,\n json : Any | None = None\n) -> Response: ...\n```\n---\nSends a GET request. Returns `Response` object. \n \n:param url: URL for the new `Request` object. \n:param params: (optional) Dictionary, list of tuples or bytes to send \nin the query string for the `Request`. \n:param \\*\\*kwargs: Optional arguments that ``request`` takes. \n:rtype: requests.Response\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/sessions.pyi#L115,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1902,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [float](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L735,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L566,7) | [SupportsItems](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/_typeshed/__init__.pyi#L164,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L862,7) | [SupportsRead](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/_typeshed/__init__.pyi#L269,7) | [list](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3464,7) | [MutableMapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L885,7) | [RequestsCookieJar](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/cookies.pyi#L38,7) | [PreparedRequest](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/models.pyi#L87,7) | [AuthBase](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/auth.pyi#L14,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [Response](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/models.pyi#L117,7)"}}} -{"suite": "web", "label": "edit response then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 11, "character": 20, "iteration": 5, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) get: def get(\n self : Session,\n url : bytes | str,\n *,\n params : Iterable[tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None]] | SupportsItems[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | bytes | str | tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | None = ...,\n data : Iterable[bytes] | Mapping[Any, Any] | SupportsRead[bytes | str] | bytes | list[tuple[Any, Any]] | str | tuple[tuple[Any, Any], ...] | None = ...,\n headers : Mapping[str, bytes | str | None] | None = ...,\n cookies : MutableMapping[str, str] | RequestsCookieJar | None = ...,\n files : Iterable[tuple[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]]] | Mapping[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]] | None = ...,\n auth : ((PreparedRequest) -> PreparedRequest) | AuthBase | tuple[str, str] | None = ...,\n timeout : float | tuple[float | None, float | None] | None = ...,\n allow_redirects: bool = ...,\n proxies : MutableMapping[str, str] | None = ...,\n hooks : Mapping[str, ((Response) -> Any) | Iterable[(Response) -> Any]] | None = ...,\n stream : bool | None = ...,\n verify : bool | str | None = ...,\n cert : str | tuple[str, str] | None = ...,\n json : Any | None = None\n) -> Response: ...\n```\n---\nSends a GET request. Returns `Response` object. \n \n:param url: URL for the new `Request` object. \n:param params: (optional) Dictionary, list of tuples or bytes to send \nin the query string for the `Request`. \n:param \\*\\*kwargs: Optional arguments that ``request`` takes. \n:rtype: requests.Response\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/sessions.pyi#L115,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1902,7) | [str](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L1033,7) | [float](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L735,7) | [int](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L418,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L566,7) | [SupportsItems](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/_typeshed/__init__.pyi#L164,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L862,7) | [SupportsRead](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/_typeshed/__init__.pyi#L269,7) | [list](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3464,7) | [MutableMapping](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/typing.pyi#L885,7) | [RequestsCookieJar](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/cookies.pyi#L38,7) | [PreparedRequest](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/models.pyi#L87,7) | [AuthBase](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/auth.pyi#L14,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_2e94b8840ea5/builtins.pyi#L3200,7) | [Response](file:///tmp/pyrefly_bundled_typeshed_third_party_015d15d3b4f3/requests/models.pyi#L117,7)"}}} +{"suite": "web", "label": "edit response then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 11, "character": 20, "iteration": 1, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) get: def get(\n self : Session,\n url : bytes | str,\n *,\n params : Iterable[tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None]] | SupportsItems[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | bytes | str | tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | None = ...,\n data : Iterable[bytes] | Mapping[Any, Any] | SupportsRead[bytes | str] | bytes | list[tuple[Any, Any]] | str | tuple[tuple[Any, Any], ...] | None = ...,\n headers : Mapping[str, bytes | str | None] | None = ...,\n cookies : MutableMapping[str, str] | RequestsCookieJar | None = ...,\n files : Iterable[tuple[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]]] | Mapping[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]] | None = ...,\n auth : ((PreparedRequest) -> PreparedRequest) | AuthBase | tuple[str, str] | None = ...,\n timeout : float | tuple[float | None, float | None] | None = ...,\n allow_redirects: bool = ...,\n proxies : MutableMapping[str, str] | None = ...,\n hooks : Mapping[str, ((Response) -> Any) | Iterable[(Response) -> Any]] | None = ...,\n stream : bool | None = ...,\n verify : bool | str | None = ...,\n cert : str | tuple[str, str] | None = ...,\n json : Any | None = None\n) -> Response: ...\n```\n---\nSends a GET request. Returns `Response` object. \n \n:param url: URL for the new `Request` object. \n:param params: (optional) Dictionary, list of tuples or bytes to send \nin the query string for the `Request`. \n:param \\*\\*kwargs: Optional arguments that ``request`` takes. \n:rtype: requests.Response\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/sessions.pyi#L115,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1902,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [float](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L735,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L566,7) | [SupportsItems](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/_typeshed/__init__.pyi#L164,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L862,7) | [SupportsRead](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/_typeshed/__init__.pyi#L269,7) | [list](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3464,7) | [MutableMapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L885,7) | [RequestsCookieJar](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/cookies.pyi#L38,7) | [PreparedRequest](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/models.pyi#L87,7) | [AuthBase](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/auth.pyi#L14,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [Response](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/models.pyi#L117,7)"}}} +{"suite": "web", "label": "edit response then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 11, "character": 20, "iteration": 2, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) get: def get(\n self : Session,\n url : bytes | str,\n *,\n params : Iterable[tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None]] | SupportsItems[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | bytes | str | tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | None = ...,\n data : Iterable[bytes] | Mapping[Any, Any] | SupportsRead[bytes | str] | bytes | list[tuple[Any, Any]] | str | tuple[tuple[Any, Any], ...] | None = ...,\n headers : Mapping[str, bytes | str | None] | None = ...,\n cookies : MutableMapping[str, str] | RequestsCookieJar | None = ...,\n files : Iterable[tuple[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]]] | Mapping[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]] | None = ...,\n auth : ((PreparedRequest) -> PreparedRequest) | AuthBase | tuple[str, str] | None = ...,\n timeout : float | tuple[float | None, float | None] | None = ...,\n allow_redirects: bool = ...,\n proxies : MutableMapping[str, str] | None = ...,\n hooks : Mapping[str, ((Response) -> Any) | Iterable[(Response) -> Any]] | None = ...,\n stream : bool | None = ...,\n verify : bool | str | None = ...,\n cert : str | tuple[str, str] | None = ...,\n json : Any | None = None\n) -> Response: ...\n```\n---\nSends a GET request. Returns `Response` object. \n \n:param url: URL for the new `Request` object. \n:param params: (optional) Dictionary, list of tuples or bytes to send \nin the query string for the `Request`. \n:param \\*\\*kwargs: Optional arguments that ``request`` takes. \n:rtype: requests.Response\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/sessions.pyi#L115,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1902,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [float](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L735,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L566,7) | [SupportsItems](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/_typeshed/__init__.pyi#L164,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L862,7) | [SupportsRead](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/_typeshed/__init__.pyi#L269,7) | [list](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3464,7) | [MutableMapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L885,7) | [RequestsCookieJar](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/cookies.pyi#L38,7) | [PreparedRequest](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/models.pyi#L87,7) | [AuthBase](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/auth.pyi#L14,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [Response](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/models.pyi#L117,7)"}}} +{"suite": "web", "label": "edit response then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 11, "character": 20, "iteration": 3, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) get: def get(\n self : Session,\n url : bytes | str,\n *,\n params : Iterable[tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None]] | SupportsItems[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | bytes | str | tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | None = ...,\n data : Iterable[bytes] | Mapping[Any, Any] | SupportsRead[bytes | str] | bytes | list[tuple[Any, Any]] | str | tuple[tuple[Any, Any], ...] | None = ...,\n headers : Mapping[str, bytes | str | None] | None = ...,\n cookies : MutableMapping[str, str] | RequestsCookieJar | None = ...,\n files : Iterable[tuple[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]]] | Mapping[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]] | None = ...,\n auth : ((PreparedRequest) -> PreparedRequest) | AuthBase | tuple[str, str] | None = ...,\n timeout : float | tuple[float | None, float | None] | None = ...,\n allow_redirects: bool = ...,\n proxies : MutableMapping[str, str] | None = ...,\n hooks : Mapping[str, ((Response) -> Any) | Iterable[(Response) -> Any]] | None = ...,\n stream : bool | None = ...,\n verify : bool | str | None = ...,\n cert : str | tuple[str, str] | None = ...,\n json : Any | None = None\n) -> Response: ...\n```\n---\nSends a GET request. Returns `Response` object. \n \n:param url: URL for the new `Request` object. \n:param params: (optional) Dictionary, list of tuples or bytes to send \nin the query string for the `Request`. \n:param \\*\\*kwargs: Optional arguments that ``request`` takes. \n:rtype: requests.Response\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/sessions.pyi#L115,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1902,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [float](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L735,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L566,7) | [SupportsItems](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/_typeshed/__init__.pyi#L164,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L862,7) | [SupportsRead](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/_typeshed/__init__.pyi#L269,7) | [list](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3464,7) | [MutableMapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L885,7) | [RequestsCookieJar](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/cookies.pyi#L38,7) | [PreparedRequest](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/models.pyi#L87,7) | [AuthBase](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/auth.pyi#L14,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [Response](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/models.pyi#L117,7)"}}} +{"suite": "web", "label": "edit response then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 11, "character": 20, "iteration": 4, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) get: def get(\n self : Session,\n url : bytes | str,\n *,\n params : Iterable[tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None]] | SupportsItems[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | bytes | str | tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | None = ...,\n data : Iterable[bytes] | Mapping[Any, Any] | SupportsRead[bytes | str] | bytes | list[tuple[Any, Any]] | str | tuple[tuple[Any, Any], ...] | None = ...,\n headers : Mapping[str, bytes | str | None] | None = ...,\n cookies : MutableMapping[str, str] | RequestsCookieJar | None = ...,\n files : Iterable[tuple[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]]] | Mapping[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]] | None = ...,\n auth : ((PreparedRequest) -> PreparedRequest) | AuthBase | tuple[str, str] | None = ...,\n timeout : float | tuple[float | None, float | None] | None = ...,\n allow_redirects: bool = ...,\n proxies : MutableMapping[str, str] | None = ...,\n hooks : Mapping[str, ((Response) -> Any) | Iterable[(Response) -> Any]] | None = ...,\n stream : bool | None = ...,\n verify : bool | str | None = ...,\n cert : str | tuple[str, str] | None = ...,\n json : Any | None = None\n) -> Response: ...\n```\n---\nSends a GET request. Returns `Response` object. \n \n:param url: URL for the new `Request` object. \n:param params: (optional) Dictionary, list of tuples or bytes to send \nin the query string for the `Request`. \n:param \\*\\*kwargs: Optional arguments that ``request`` takes. \n:rtype: requests.Response\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/sessions.pyi#L115,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1902,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [float](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L735,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L566,7) | [SupportsItems](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/_typeshed/__init__.pyi#L164,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L862,7) | [SupportsRead](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/_typeshed/__init__.pyi#L269,7) | [list](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3464,7) | [MutableMapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L885,7) | [RequestsCookieJar](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/cookies.pyi#L38,7) | [PreparedRequest](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/models.pyi#L87,7) | [AuthBase](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/auth.pyi#L14,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [Response](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/models.pyi#L117,7)"}}} +{"suite": "web", "label": "edit response then hover (edit+hover)", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/web/src/app.py", "line": 11, "character": 20, "iteration": 5, "result": {"canIncreaseVerbosity": false, "contents": {"kind": "markdown", "value": "```python\n(method) get: def get(\n self : Session,\n url : bytes | str,\n *,\n params : Iterable[tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None]] | SupportsItems[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | bytes | str | tuple[bytes | float | int | str, Iterable[bytes | float | int | str] | bytes | float | int | str | None] | None = ...,\n data : Iterable[bytes] | Mapping[Any, Any] | SupportsRead[bytes | str] | bytes | list[tuple[Any, Any]] | str | tuple[tuple[Any, Any], ...] | None = ...,\n headers : Mapping[str, bytes | str | None] | None = ...,\n cookies : MutableMapping[str, str] | RequestsCookieJar | None = ...,\n files : Iterable[tuple[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]]] | Mapping[str, SupportsRead[bytes | str] | bytes | str | tuple[str | None, SupportsRead[bytes | str] | bytes | str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str] | tuple[str | None, SupportsRead[bytes | str] | bytes | str, str, Mapping[str, str]]] | None = ...,\n auth : ((PreparedRequest) -> PreparedRequest) | AuthBase | tuple[str, str] | None = ...,\n timeout : float | tuple[float | None, float | None] | None = ...,\n allow_redirects: bool = ...,\n proxies : MutableMapping[str, str] | None = ...,\n hooks : Mapping[str, ((Response) -> Any) | Iterable[(Response) -> Any]] | None = ...,\n stream : bool | None = ...,\n verify : bool | str | None = ...,\n cert : str | tuple[str, str] | None = ...,\n json : Any | None = None\n) -> Response: ...\n```\n---\nSends a GET request. Returns `Response` object. \n \n:param url: URL for the new `Request` object. \n:param params: (optional) Dictionary, list of tuples or bytes to send \nin the query string for the `Request`. \n:param \\*\\*kwargs: Optional arguments that ``request`` takes. \n:rtype: requests.Response\n\nGo to [Session](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/sessions.pyi#L115,7) | [bytes](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1902,7) | [str](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L1033,7) | [float](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L735,7) | [int](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L418,7) | [Iterable](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L566,7) | [SupportsItems](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/_typeshed/__init__.pyi#L164,7) | [Mapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L862,7) | [SupportsRead](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/_typeshed/__init__.pyi#L269,7) | [list](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3464,7) | [MutableMapping](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/typing.pyi#L885,7) | [RequestsCookieJar](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/cookies.pyi#L38,7) | [PreparedRequest](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/models.pyi#L87,7) | [AuthBase](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/auth.pyi#L14,7) | [bool](file:///tmp/pyrefly_bundled_typeshed_4f6b543e6b61/builtins.pyi#L3200,7) | [Response](file:///tmp/pyrefly_bundled_typeshed_third_party_2044215f1cdd/requests/models.pyi#L117,7)"}}} diff --git a/latest-results/pyrefly-20260928T060730Z.json b/latest-results/pyrefly-20260929T060553Z.json similarity index 91% rename from latest-results/pyrefly-20260928T060730Z.json rename to latest-results/pyrefly-20260929T060553Z.json index 2dcfb7d..f45236d 100644 --- a/latest-results/pyrefly-20260928T060730Z.json +++ b/latest-results/pyrefly-20260929T060553Z.json @@ -17,8 +17,8 @@ "tsp_semantic", "web" ], - "started_at_unix": 1790575746.3662646, - "finished_at_unix": 1790575782.6982367, + "started_at_unix": 1790662028.9449375, + "finished_at_unix": 1790662057.4888895, "scenario_reports": [], "benchmark_reports": [ { @@ -31,7 +31,7 @@ "environment_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv", "python_executable": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/data_science/.venv/bin/python", "success": true, - "total_duration_ms": 1053.9026360000037, + "total_duration_ms": 819.4783309999139, "error_message": null, "summary": { "request_count": 32, @@ -40,11 +40,11 @@ "failure_count": 0, "bytes_sent": 18656, "bytes_received": 1424003, - "min_ms": 0.17612900001040543, - "max_ms": 411.0243819999937, - "mean_ms": 31.593634156248385, - "median_ms": 2.241412999993031, - "p95_ms": 194.3210184999856, + "min_ms": 0.14154199993754446, + "max_ms": 339.20091899994986, + "mean_ms": 24.44194715624093, + "median_ms": 1.7197784999893884, + "p95_ms": 150.20251849995248, "result_summary": { "present_count": 31, "empty_count": 1, @@ -99,27 +99,27 @@ "by_method": { "textDocument/completion": { "point_count": 2, - "min_ms": 0.7038859999966007, - "max_ms": 411.0243819999937, - "mean_ms": 51.43391480000048, - "median_ms": 18.288666999993097, - "p95_ms": 236.47803279999198 + "min_ms": 0.6633059999785473, + "max_ms": 339.20091899994986, + "mean_ms": 41.82463340000595, + "median_ms": 13.289129000042976, + "p95_ms": 194.634857699975 }, "textDocument/hover": { "point_count": 2, - "min_ms": 0.6545809999920493, - "max_ms": 17.834840000006125, - "mean_ms": 5.297738899997739, - "median_ms": 2.3208660000051395, - "p95_ms": 14.0843240000052 + "min_ms": 0.33990999997968174, + "max_ms": 1.951190999989194, + "mean_ms": 1.133375500000966, + "median_ms": 1.2224535000200376, + "p95_ms": 1.9295405999628201 }, "textDocument/definition": { "point_count": 1, - "min_ms": 0.17612900001040543, - "max_ms": 0.19456300000797455, - "mean_ms": 0.18935740000074475, - "median_ms": 0.1919080000050144, - "p95_ms": 0.19451100000651422 + "min_ms": 0.1700650000202586, + "max_ms": 0.292167999987214, + "mean_ms": 0.21505699999124772, + "median_ms": 0.20577999998749874, + "p95_ms": 0.2761139999847728 } }, "validation": { @@ -145,11 +145,11 @@ "failure_count": 0, "bytes_sent": 1425, "bytes_received": 95745, - "min_ms": 0.7038859999966007, - "max_ms": 411.0243819999937, - "mean_ms": 83.23008940000136, - "median_ms": 1.549108000006072, - "p95_ms": 329.23834719999826, + "min_ms": 0.6633059999785473, + "max_ms": 339.20091899994986, + "mean_ms": 69.13779699998486, + "median_ms": 1.4406340000050477, + "p95_ms": 272.09671279995285, "result_summary": { "present_count": 5, "empty_count": 0, @@ -209,9 +209,9 @@ { "kind": "request", "method": "textDocument/completion", - "duration_ms": 181.73537799998485, + "duration_ms": 155.41284999994787, "success": true, - "started_at_unix": 1790575746.3819523, + "started_at_unix": 1790662028.958936, "bytes_sent": 285, "bytes_received": 19149, "request_id": 2, @@ -239,9 +239,9 @@ { "kind": "request", "method": "textDocument/completion", - "duration_ms": 411.0243819999937, + "duration_ms": 339.20091899994986, "success": true, - "started_at_unix": 1790575746.5643935, + "started_at_unix": 1790662029.114762, "bytes_sent": 285, "bytes_received": 19149, "request_id": 3, @@ -269,9 +269,9 @@ { "kind": "request", "method": "textDocument/completion", - "duration_ms": 0.7038859999966007, + "duration_ms": 3.6798879999651035, "success": true, - "started_at_unix": 1790575746.9762375, + "started_at_unix": 1790662029.4548168, "bytes_sent": 285, "bytes_received": 19149, "request_id": 4, @@ -299,9 +299,9 @@ { "kind": "request", "method": "textDocument/completion", - "duration_ms": 0.7788629999936347, + "duration_ms": 1.4406340000050477, "success": true, - "started_at_unix": 1790575746.9776034, + "started_at_unix": 1790662029.4594014, "bytes_sent": 285, "bytes_received": 19149, "request_id": 5, @@ -329,9 +329,9 @@ { "kind": "request", "method": "textDocument/completion", - "duration_ms": 1.549108000006072, + "duration_ms": 0.6633059999785473, "success": true, - "started_at_unix": 1790575746.979664, + "started_at_unix": 1790662029.461747, "bytes_sent": 285, "bytes_received": 19149, "request_id": 6, @@ -359,9 +359,9 @@ { "kind": "request", "method": "textDocument/completion", - "duration_ms": 2.094208000016806, + "duration_ms": 0.7042380000257253, "success": true, - "started_at_unix": 1790575746.982508, + "started_at_unix": 1790662029.4633136, "bytes_sent": 285, "bytes_received": 19149, "request_id": 7, @@ -404,11 +404,11 @@ "failure_count": 0, "bytes_sent": 1269, "bytes_received": 16614, - "min_ms": 2.165571000006139, - "max_ms": 2.3388440000076116, - "mean_ms": 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"started_at_unix": 1790575741.0180998, + "started_at_unix": 1790662024.6342802, "bytes_sent": 319, "bytes_received": 0, "request_id": null, @@ -17080,9 +17080,9 @@ { "kind": "notification", "method": "textDocument/didChange", - "duration_ms": 0.015829999995276012, + "duration_ms": 0.010997000003953872, "success": true, - "started_at_unix": 1790575741.0181394, + "started_at_unix": 1790662024.6343136, "bytes_sent": 374, "bytes_received": 0, "request_id": null, @@ -17099,9 +17099,9 @@ { "kind": "request", "method": "textDocument/hover", - "duration_ms": 39.86654600001316, + "duration_ms": 29.499292999958016, "success": true, - "started_at_unix": 1790575741.018164, + "started_at_unix": 1790662024.6343312, "bytes_sent": 241, "bytes_received": 614, "request_id": 29, @@ -17129,9 +17129,9 @@ { "kind": "notification", "method": "textDocument/didChange", - "duration_ms": 0.04046600000151557, + "duration_ms": 0.023846000090088637, "success": true, - "started_at_unix": 1790575741.05816, + "started_at_unix": 1790662024.663928, "bytes_sent": 319, "bytes_received": 0, "request_id": null, @@ -17148,9 +17148,9 @@ { "kind": "notification", "method": "textDocument/didChange", - "duration_ms": 0.023874999982353984, + "duration_ms": 0.011618000030466646, "success": true, - "started_at_unix": 1790575741.058214, + "started_at_unix": 1790662024.6639602, "bytes_sent": 374, "bytes_received": 0, "request_id": null, @@ -17167,9 +17167,9 @@ { "kind": "request", "method": "textDocument/hover", - "duration_ms": 31.6715080000165, + "duration_ms": 21.763535999980377, "success": true, - "started_at_unix": 1790575741.0582519, + "started_at_unix": 1790662024.6639795, "bytes_sent": 241, "bytes_received": 614, "request_id": 30, @@ -17197,9 +17197,9 @@ { "kind": "notification", "method": "textDocument/didChange", - "duration_ms": 0.042930000006435876, + "duration_ms": 0.029605000008814386, "success": true, - "started_at_unix": 1790575741.0900507, + "started_at_unix": 1790662024.6858432, "bytes_sent": 319, "bytes_received": 0, "request_id": null, @@ -17216,9 +17216,9 @@ { "kind": "notification", "method": "textDocument/didChange", - "duration_ms": 0.02351300000213996, + "duration_ms": 0.01559299994369212, "success": true, - "started_at_unix": 1790575741.0901074, + "started_at_unix": 1790662024.6858823, "bytes_sent": 374, "bytes_received": 0, "request_id": null, @@ -17235,9 +17235,9 @@ { "kind": "request", "method": "textDocument/hover", - "duration_ms": 36.19914699999072, + "duration_ms": 23.718352999935632, "success": true, - "started_at_unix": 1790575741.090144, + "started_at_unix": 1790662024.6859066, "bytes_sent": 241, "bytes_received": 614, "request_id": 31, @@ -17265,9 +17265,9 @@ { "kind": "notification", "method": "textDocument/didChange", - "duration_ms": 0.032821000019112034, + "duration_ms": 0.02343500000279164, "success": true, - "started_at_unix": 1790575741.1264558, + "started_at_unix": 1790662024.7097123, "bytes_sent": 319, "bytes_received": 0, "request_id": null, @@ -17284,9 +17284,9 @@ { "kind": "notification", "method": "textDocument/didClose", - "duration_ms": 0.01913599999170401, + "duration_ms": 0.011066999945796852, "success": true, - "started_at_unix": 1790575741.1266003, + "started_at_unix": 1790662024.7098174, "bytes_sent": 198, "bytes_received": 0, "request_id": null, @@ -17299,9 +17299,9 @@ { "kind": "request", "method": "shutdown", - "duration_ms": 2.274209000006522, + "duration_ms": 2.0751380000092468, "success": true, - "started_at_unix": 1790575741.1266277, + "started_at_unix": 1790662024.7098331, "bytes_sent": 67, "bytes_received": 61, "request_id": 32, @@ -17319,9 +17319,9 @@ { "kind": "notification", "method": "exit", - "duration_ms": 0.037208999998483705, + "duration_ms": 0.024957000050562783, "success": true, - "started_at_unix": 1790575741.1289265, + "started_at_unix": 1790662024.7119296, "bytes_sent": 55, "bytes_received": 0, "request_id": null, diff --git a/latest-results/summary-20260928T060730Z.csv b/latest-results/summary-20260928T060730Z.csv deleted file mode 100644 index cc2972c..0000000 --- a/latest-results/summary-20260928T060730Z.csv +++ /dev/null @@ -1,132 +0,0 @@ -report_type,baseline_server_id,server_id,server_name,suite_name,scenario_name,point_label,method,method_display,success,mean_ms,p95_ms,non_empty_rate,result_metric_name,result_metric_label,result_metric_value,result_metric_delta,validation_passed,validation_failure_count -benchmark,pyright,pyright,Pyright,data_science,,dataframe completion,textDocument/completion,textDocument/completion,True,6.119325600002412,8.839870800005656,1.0,completion_item_count,Completions found,201.0,0.0,True,0 -benchmark,pyright,ty,Ty,data_science,,dataframe completion,textDocument/completion,textDocument/completion,True,1.6489957999965554,1.8898415999899496,1.0,completion_item_count,Completions found,223.0,22.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,data_science,,dataframe completion,textDocument/completion,textDocument/completion,True,83.23008940000136,329.23834719999826,1.0,completion_item_count,Completions found,250.0,49.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,data_science,,dataframe completion,textDocument/completion,textDocument/completion,True,227.49525020000192,468.36264900001646,1.0,completion_item_count,Completions found,188.0,-13.0,True,0 -benchmark,pyright,pyright,Pyright,data_science,,dataframe describe hover,textDocument/hover,textDocument/hover,True,1.1646368000072016,1.4907686000100284,1.0,hover_text_char_count,Hover length,4019.0,0.0,True,0 -benchmark,pyright,ty,Ty,data_science,,dataframe describe hover,textDocument/hover,textDocument/hover,True,0.3297655999915605,0.3563487999940662,1.0,hover_text_char_count,Hover length,4232.0,213.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,data_science,,dataframe describe hover,textDocument/hover,textDocument/hover,True,2.2333937999974296,2.331652800006623,1.0,hover_text_char_count,Hover length,3182.0,-837.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,data_science,,dataframe describe hover,textDocument/hover,textDocument/hover,True,175.41739119999988,175.90426179999668,1.0,hover_text_char_count,Hover length,4134.0,115.0,True,0 -benchmark,pyright,pyright,Pyright,data_science,,summarize definition,textDocument/definition,textDocument/definition,True,0.3948263999973278,0.44155320000243137,1.0,location_count,Definitions found,1.0,0.0,True,0 -benchmark,pyright,ty,Ty,data_science,,summarize definition,textDocument/definition,textDocument/definition,True,0.21245460000045568,0.2284362000125384,1.0,location_count,Definitions found,1.0,0.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,data_science,,summarize definition,textDocument/definition,textDocument/definition,True,0.18935740000074475,0.19451100000651422,1.0,location_count,Definitions found,1.0,0.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,data_science,,summarize definition,textDocument/definition,textDocument/definition,True,1.034802800000989,1.0557118000008359,1.0,location_count,Definitions found,1.0,0.0,True,0 -benchmark,pyright,pyright,Pyright,data_science,,edit array then complete (edit+completion),textDocument/completion,textDocument/completion,True,329.84938479999926,464.41747540000046,1.0,completion_item_count,Completions found,168.0,0.0,True,0 -benchmark,pyright,ty,Ty,data_science,,edit array then complete (edit+completion),textDocument/completion,textDocument/completion,True,12.594198399995093,14.170623000001115,1.0,completion_item_count,Completions found,168.0,0.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,data_science,,edit array then complete (edit+completion),textDocument/completion,textDocument/completion,True,19.637740199999598,22.446893999995154,1.0,completion_item_count,Completions found,149.0,-19.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,data_science,,edit array then complete (edit+completion),textDocument/completion,textDocument/completion,False,4.286280200000192,4.357377000002316,0.0,completion_item_count,Completions found,0.0,-168.0,False,10 -benchmark,pyright,pyright,Pyright,data_science,,edit array then hover (edit+hover),textDocument/hover,textDocument/hover,True,32.33889400000294,35.76522020001107,1.0,hover_text_char_count,Hover length,278.0,0.0,True,0 -benchmark,pyright,ty,Ty,data_science,,edit array then hover (edit+hover),textDocument/hover,textDocument/hover,True,5.663309200002686,5.788214400007519,1.0,hover_text_char_count,Hover length,267.0,-11.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,data_science,,edit array then hover (edit+hover),textDocument/hover,textDocument/hover,True,8.362083999998049,16.16794400000572,1.0,hover_text_char_count,Hover length,2546.0,2268.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,data_science,,edit array then hover (edit+hover),textDocument/hover,textDocument/hover,True,166.70991700000286,168.72076660000062,1.0,hover_text_char_count,Hover length,5662.0,5384.0,True,0 -benchmark,pyright,pyright,Pyright,django,,queryset completion,textDocument/completion,textDocument/completion,True,4.732433600003105,7.801473199998553,1.0,completion_item_count,Completions found,10.0,0.0,True,0 -benchmark,pyright,ty,Ty,django,,queryset completion,textDocument/completion,textDocument/completion,True,4.813258600000836,7.156921800003601,1.0,completion_item_count,Completions found,261.0,251.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,django,,queryset completion,textDocument/completion,textDocument/completion,True,17.73992119999548,68.19672679999938,1.0,completion_item_count,Completions found,15.0,5.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,django,,queryset completion,textDocument/completion,textDocument/completion,True,209.10146239999676,640.3891957999916,1.0,completion_item_count,Completions found,2.0,-8.0,True,0 -benchmark,pyright,pyright,Pyright,django,,queryset filter hover,textDocument/hover,textDocument/hover,True,0.5189921999999569,0.5860228000017287,1.0,hover_text_char_count,Hover length,57.0,0.0,True,0 -benchmark,pyright,ty,Ty,django,,queryset filter hover,textDocument/hover,textDocument/hover,True,0.22957459999588536,0.25141339999663614,1.0,hover_text_char_count,Hover length,46.0,-11.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,django,,queryset filter hover,textDocument/hover,textDocument/hover,True,0.4177557999980763,0.4680139999948096,1.0,hover_text_char_count,Hover length,298.0,241.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,django,,queryset filter hover,textDocument/hover,textDocument/hover,True,151.88236179999421,152.92167899997935,1.0,hover_text_char_count,Hover length,57.0,0.0,True,0 -benchmark,pyright,pyright,Pyright,django,,model definition,textDocument/definition,textDocument/definition,True,0.37355499999591757,0.47016139999414003,1.0,location_count,Definitions found,1.0,0.0,True,0 -benchmark,pyright,ty,Ty,django,,model definition,textDocument/definition,textDocument/definition,True,0.19236519999594748,0.20353220000401961,1.0,location_count,Definitions found,1.0,0.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,django,,model definition,textDocument/definition,textDocument/definition,True,0.37237880000020596,0.41473599999903854,1.0,location_count,Definitions found,1.0,0.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,django,,model definition,textDocument/definition,textDocument/definition,True,1.0767654000005678,1.1080272000072,1.0,location_count,Definitions found,1.0,0.0,True,0 -benchmark,pyright,pyright,Pyright,django,,edit queryset then complete (edit+completion),textDocument/completion,textDocument/completion,True,25.850669799999082,28.514820600003077,1.0,completion_item_count,Completions found,104.0,0.0,True,0 -benchmark,pyright,ty,Ty,django,,edit queryset then complete (edit+completion),textDocument/completion,textDocument/completion,True,4.7258599999963735,5.037746599981574,1.0,completion_item_count,Completions found,104.0,0.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,django,,edit queryset then complete (edit+completion),textDocument/completion,textDocument/completion,True,1.9370228000013867,2.023343999996996,1.0,completion_item_count,Completions found,83.0,-21.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,django,,edit queryset then complete (edit+completion),textDocument/completion,textDocument/completion,True,255.9143267999957,289.2440289999911,1.0,completion_item_count,Completions found,143.0,39.0,True,0 -benchmark,pyright,pyright,Pyright,django,,edit queryset then hover (edit+hover),textDocument/hover,textDocument/hover,True,38.272985399999015,43.628796399994485,1.0,hover_text_char_count,Hover length,83.0,0.0,True,0 -benchmark,pyright,ty,Ty,django,,edit queryset then hover (edit+hover),textDocument/hover,textDocument/hover,True,3.158542200003467,3.195836199989799,1.0,hover_text_char_count,Hover length,100.0,17.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,django,,edit queryset then hover (edit+hover),textDocument/hover,textDocument/hover,True,0.793426800009911,0.8506656000179191,1.0,hover_text_char_count,Hover length,858.0,775.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,django,,edit queryset then hover (edit+hover),textDocument/hover,textDocument/hover,True,242.02036119999661,243.46662020000167,1.0,hover_text_char_count,Hover length,71.0,-12.0,True,0 -benchmark,pyright,pyright,Pyright,pandas,,report dataframe completion,textDocument/completion,textDocument/completion,True,79.0691006000003,266.9421858000049,1.0,completion_item_count,Completions found,271.2,0.0,True,0 -benchmark,pyright,ty,Ty,pandas,,report dataframe completion,textDocument/completion,textDocument/completion,True,18.277907800001003,21.808449800010976,1.0,completion_item_count,Completions found,1000.0,728.8,True,0 -benchmark,pyright,pyrefly,Pyrefly,pandas,,report dataframe completion,textDocument/completion,textDocument/completion,True,87.86251519999837,347.6151127999855,1.0,completion_item_count,Completions found,16.0,-255.2,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,pandas,,report dataframe completion,textDocument/completion,textDocument/completion,True,92.22980880000478,216.41691039998813,1.0,completion_item_count,Completions found,6.0,-265.2,True,0 -benchmark,pyright,pyright,Pyright,pandas,,dataframe groupby hover,textDocument/hover,textDocument/hover,True,0.6757945999964932,0.7548677999920983,1.0,hover_text_char_count,Hover length,350.0,0.0,True,0 -benchmark,pyright,ty,Ty,pandas,,dataframe groupby hover,textDocument/hover,textDocument/hover,True,0.2672146000122666,0.2923776000102407,1.0,hover_text_char_count,Hover length,329.0,-21.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,pandas,,dataframe groupby hover,textDocument/hover,textDocument/hover,True,3.848359800002754,5.7408227999872,1.0,hover_text_char_count,Hover length,2759.0,2409.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,pandas,,dataframe groupby hover,textDocument/hover,textDocument/hover,True,191.16486080000072,192.66575120000198,1.0,hover_text_char_count,Hover length,301.0,-49.0,True,0 -benchmark,pyright,pyright,Pyright,pandas,,build report definition,textDocument/definition,textDocument/definition,True,0.3967577999929972,0.4679387999914297,1.0,location_count,Definitions found,1.0,0.0,True,0 -benchmark,pyright,ty,Ty,pandas,,build report definition,textDocument/definition,textDocument/definition,True,0.20255999999676533,0.20768759998759379,1.0,location_count,Definitions found,1.0,0.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,pandas,,build report definition,textDocument/definition,textDocument/definition,True,2.9220543999940674,2.932926199986241,1.0,location_count,Definitions found,1.0,0.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,pandas,,build report definition,textDocument/definition,textDocument/definition,True,1.030108200001223,1.0485845999937737,1.0,location_count,Definitions found,1.0,0.0,True,0 -benchmark,pyright,pyright,Pyright,pandas,,edit dataframe then complete (edit+completion),textDocument/completion,textDocument/completion,True,838.4930983999993,1379.1481671999943,1.0,completion_item_count,Completions found,440.0,0.0,True,0 -benchmark,pyright,ty,Ty,pandas,,edit dataframe then complete (edit+completion),textDocument/completion,textDocument/completion,True,15.862748400013515,17.413950999997496,1.0,completion_item_count,Completions found,448.0,8.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,pandas,,edit dataframe then complete (edit+completion),textDocument/completion,textDocument/completion,True,47.65374120000274,80.11805680000066,1.0,completion_item_count,Completions found,256.0,-184.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,pandas,,edit dataframe then complete (edit+completion),textDocument/completion,textDocument/completion,True,215.62128779999625,216.90200520000076,1.0,completion_item_count,Completions found,441.0,1.0,True,0 -benchmark,pyright,pyright,Pyright,pandas,,edit dataframe then hover (edit+hover),textDocument/hover,textDocument/hover,True,11.32426480000106,13.286811000006082,1.0,hover_text_char_count,Hover length,4292.0,0.0,True,0 -benchmark,pyright,ty,Ty,pandas,,edit dataframe then hover (edit+hover),textDocument/hover,textDocument/hover,True,4.4666256000027715,4.47725159999095,1.0,hover_text_char_count,Hover length,4441.0,149.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,pandas,,edit dataframe then hover (edit+hover),textDocument/hover,textDocument/hover,True,5.566680199996199,13.191992999986724,1.0,hover_text_char_count,Hover length,943.0,-3349.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,pandas,,edit dataframe then hover (edit+hover),textDocument/hover,textDocument/hover,True,181.4526525999952,183.4398725999904,1.0,hover_text_char_count,Hover length,232.0,-4060.0,True,0 -benchmark,pyright,pyright,Pyright,sqlalchemy,,query completion,textDocument/completion,textDocument/completion,True,14.417787000002136,26.580071600002952,1.0,completion_item_count,Completions found,1.0,0.0,True,0 -benchmark,pyright,ty,Ty,sqlalchemy,,query completion,textDocument/completion,textDocument/completion,True,3.3298360000060256,7.410486400016223,1.0,completion_item_count,Completions found,1.0,0.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,sqlalchemy,,query completion,textDocument/completion,textDocument/completion,True,88.09765879999532,351.555751599983,1.0,completion_item_count,Completions found,15.0,14.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,sqlalchemy,,query completion,textDocument/completion,textDocument/completion,True,208.8318748000006,460.44303460000145,1.0,completion_item_count,Completions found,1.0,0.0,True,0 -benchmark,pyright,pyright,Pyright,sqlalchemy,,sessionmaker hover,textDocument/hover,textDocument/hover,True,4.253389799998786,7.787071399999944,1.0,hover_text_char_count,Hover length,10579.0,0.0,True,0 -benchmark,pyright,ty,Ty,sqlalchemy,,sessionmaker hover,textDocument/hover,textDocument/hover,True,0.44086479999805306,0.5130642000040098,1.0,hover_text_char_count,Hover length,10621.0,42.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,sqlalchemy,,sessionmaker hover,textDocument/hover,textDocument/hover,True,2.4916614000005666,3.317949199998793,1.0,hover_text_char_count,Hover length,15239.0,4660.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,sqlalchemy,,sessionmaker hover,textDocument/hover,textDocument/hover,True,318.44260099999815,321.9359316000066,1.0,hover_text_char_count,Hover length,10498.0,-81.0,True,0 -benchmark,pyright,pyright,Pyright,sqlalchemy,,mapped class definition,textDocument/definition,textDocument/definition,True,0.7548323999998274,1.6478948000070655,1.0,location_count,Definitions found,1.0,0.0,True,0 -benchmark,pyright,ty,Ty,sqlalchemy,,mapped class definition,textDocument/definition,textDocument/definition,True,0.21077099999047277,0.22708959997999045,1.0,location_count,Definitions found,2.0,1.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,sqlalchemy,,mapped class definition,textDocument/definition,textDocument/definition,True,2.1982319999949596,3.669659599995611,1.0,location_count,Definitions found,1.0,0.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,sqlalchemy,,mapped class definition,textDocument/definition,textDocument/definition,True,1.0795691999987866,1.1064213999929962,1.0,location_count,Definitions found,1.0,0.0,True,0 -benchmark,pyright,pyright,Pyright,sqlalchemy,,edit query then complete (edit+completion),textDocument/completion,textDocument/completion,True,145.72185920000322,176.38167200000225,1.0,completion_item_count,Completions found,38.0,0.0,True,0 -benchmark,pyright,ty,Ty,sqlalchemy,,edit query then complete (edit+completion),textDocument/completion,textDocument/completion,True,5.357676399995626,5.555322000003571,1.0,completion_item_count,Completions found,23.0,-15.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,sqlalchemy,,edit query then complete (edit+completion),textDocument/completion,textDocument/completion,True,7.204661800000167,22.83945419999895,1.0,completion_item_count,Completions found,17.0,-21.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,sqlalchemy,,edit query then complete (edit+completion),textDocument/completion,textDocument/completion,False,37.64422740000555,38.27115120000144,0.0,completion_item_count,Completions found,0.0,-38.0,False,10 -benchmark,pyright,pyright,Pyright,sqlalchemy,,edit session then hover (edit+hover),textDocument/hover,textDocument/hover,True,101.93516180000017,110.97852500000158,1.0,hover_text_char_count,Hover length,893.0,0.0,True,0 -benchmark,pyright,ty,Ty,sqlalchemy,,edit session then hover (edit+hover),textDocument/hover,textDocument/hover,True,5.063658800003168,5.0993475999973725,1.0,hover_text_char_count,Hover length,951.0,58.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,sqlalchemy,,edit session then hover (edit+hover),textDocument/hover,textDocument/hover,True,4.603029399993375,12.106963999997332,1.0,hover_text_char_count,Hover length,2242.0,1349.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,sqlalchemy,,edit session then hover (edit+hover),textDocument/hover,textDocument/hover,False,37.18111580000141,37.54723260000219,0.0,hover_text_char_count,Hover length,0.0,-893.0,False,10 -benchmark,pyright,pyright,Pyright,transformers,,classifier pipeline completion,textDocument/completion,textDocument/completion,True,51.52740840000547,81.65036980000764,1.0,completion_item_count,Completions found,123.0,0.0,True,0 -benchmark,pyright,ty,Ty,transformers,,classifier pipeline completion,textDocument/completion,textDocument/completion,True,11.43924759999777,12.582162399991148,1.0,completion_item_count,Completions found,777.0,654.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,transformers,,classifier pipeline completion,textDocument/completion,textDocument/completion,True,877.6658394000038,3509.785699200011,1.0,completion_item_count,Completions found,15.0,-108.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,transformers,,classifier pipeline completion,textDocument/completion,textDocument/completion,True,149.06878639999377,151.99489599999652,1.0,completion_item_count,Completions found,2.0,-121.0,True,0 -benchmark,pyright,pyright,Pyright,transformers,,pipeline hover,textDocument/hover,textDocument/hover,True,0.48414480000360527,0.5354544000113037,1.0,hover_text_char_count,Hover length,34.0,0.0,True,0 -benchmark,pyright,ty,Ty,transformers,,pipeline hover,textDocument/hover,textDocument/hover,True,0.2504216000033921,0.26589059999082565,1.0,hover_text_char_count,Hover length,7.0,-27.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,transformers,,pipeline hover,textDocument/hover,textDocument/hover,True,0.2195222000011654,0.22234760000401366,1.0,hover_text_char_count,Hover length,48.0,14.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,transformers,,pipeline hover,textDocument/hover,textDocument/hover,False,2741.0767143999806,2774.2773103999866,0.0,hover_text_char_count,Hover length,0.0,-34.0,False,10 -benchmark,pyright,pyright,Pyright,transformers,,auto tokenizer definition,textDocument/definition,textDocument/definition,True,0.45409060000451973,0.5215322000140077,1.0,location_count,Definitions found,1.0,0.0,True,0 -benchmark,pyright,ty,Ty,transformers,,auto tokenizer definition,textDocument/definition,textDocument/definition,True,0.27514939999946364,0.2835471999958372,1.0,location_count,Definitions found,1.0,0.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,transformers,,auto tokenizer definition,textDocument/definition,textDocument/definition,True,0.22215280000636994,0.22869720001494898,1.0,location_count,Definitions found,1.0,0.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,transformers,,auto tokenizer definition,textDocument/definition,textDocument/definition,True,2180.97402479998,2209.827497199967,1.0,location_count,Definitions found,1.0,0.0,True,0 -benchmark,pyright,pyright,Pyright,transformers,,edit prediction then complete (edit+completion),textDocument/completion,textDocument/completion,True,6.981431200000543,9.933740800011037,0.0,completion_item_count,Completions found,0.0,0.0,True,0 -benchmark,pyright,ty,Ty,transformers,,edit prediction then complete (edit+completion),textDocument/completion,textDocument/completion,True,6.117162200007442,6.485856799997691,1.0,completion_item_count,Completions found,23.0,23.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,transformers,,edit prediction then complete (edit+completion),textDocument/completion,textDocument/completion,True,12.666792599998189,23.94116760000884,0.0,completion_item_count,Completions found,0.0,0.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,transformers,,edit prediction then complete (edit+completion),textDocument/completion,textDocument/completion,True,2.598799399993368,2.6632334000169067,0.0,completion_item_count,Completions found,0.0,0.0,True,0 -benchmark,pyright,pyright,Pyright,transformers,,edit tokenizer then hover (edit+hover),textDocument/hover,textDocument/hover,True,480.5350553999915,505.4607397999803,1.0,hover_text_char_count,Hover length,30.0,0.0,True,0 -benchmark,pyright,ty,Ty,transformers,,edit tokenizer then hover (edit+hover),textDocument/hover,textDocument/hover,True,5.814816800000244,5.918981400009216,1.0,hover_text_char_count,Hover length,7.0,-23.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,transformers,,edit tokenizer then hover (edit+hover),textDocument/hover,textDocument/hover,True,3.045564799992917,3.7011871999766295,1.0,hover_text_char_count,Hover length,33.0,3.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,transformers,,edit tokenizer then hover (edit+hover),textDocument/hover,textDocument/hover,False,2618.6240633999887,2658.797489599999,0.0,hover_text_char_count,Hover length,0.0,-30.0,False,10 -benchmark,pyright,pyright,Pyright,web,,request args completion,textDocument/completion,textDocument/completion,True,4.714771800001927,7.832015800016733,1.0,completion_item_count,Completions found,14.0,0.0,True,0 -benchmark,pyright,ty,Ty,web,,request args completion,textDocument/completion,textDocument/completion,True,5.7357110000054945,8.140147400018805,1.0,completion_item_count,Completions found,467.0,453.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,web,,request args completion,textDocument/completion,textDocument/completion,True,54.9555555999973,182.8751745999966,1.0,completion_item_count,Completions found,487.8,473.8,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,web,,request args completion,textDocument/completion,textDocument/completion,True,70.74944259999256,96.7188948000171,1.0,completion_item_count,Completions found,1.0,-13.0,True,0 -benchmark,pyright,pyright,Pyright,web,,client session hover,textDocument/hover,textDocument/hover,True,0.584979799998564,0.6748656000013398,1.0,hover_text_char_count,Hover length,26.0,0.0,True,0 -benchmark,pyright,ty,Ty,web,,client session hover,textDocument/hover,textDocument/hover,True,0.20524120000118273,0.2254775999915637,1.0,hover_text_char_count,Hover length,7.0,-19.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,web,,client session hover,textDocument/hover,textDocument/hover,True,5.190579799995021,18.162839999996546,1.0,hover_text_char_count,Hover length,167.0,141.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,web,,client session hover,textDocument/hover,textDocument/hover,True,102.39710699997886,193.3277423999698,1.0,hover_text_char_count,Hover length,359.0,333.0,True,0 -benchmark,pyright,pyright,Pyright,web,,client references,textDocument/references,textDocument/references,True,0.8118700000011358,0.8523972000205049,1.0,location_count,References found,2.0,0.0,True,0 -benchmark,pyright,ty,Ty,web,,client references,textDocument/references,textDocument/references,True,0.5763554000054683,0.6208322000020416,1.0,location_count,References found,2.0,0.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,web,,client references,textDocument/references,textDocument/references,True,0.33787259999940034,0.3533714000013788,1.0,location_count,References found,2.0,0.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,web,,client references,textDocument/references,textDocument/references,True,33.41753099998641,84.01988699997673,1.0,location_count,References found,2.0,0.0,True,0 -benchmark,pyright,pyright,Pyright,web,,edit response then complete (edit+completion),textDocument/completion,textDocument/completion,True,4.80265620000182,5.89290560000677,1.0,completion_item_count,Completions found,205.0,0.0,True,0 -benchmark,pyright,ty,Ty,web,,edit response then complete (edit+completion),textDocument/completion,textDocument/completion,True,4.4430121999937455,4.565350999996554,1.0,completion_item_count,Completions found,225.0,20.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,web,,edit response then complete (edit+completion),textDocument/completion,textDocument/completion,True,0.5764439999950355,0.6215897999993558,1.0,completion_item_count,Completions found,32.0,-173.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,web,,edit response then complete (edit+completion),textDocument/completion,textDocument/completion,True,88.4262799999874,88.72037300001239,1.0,completion_item_count,Completions found,57.0,-148.0,True,0 -benchmark,pyright,pyright,Pyright,web,,edit response then hover (edit+hover),textDocument/hover,textDocument/hover,True,36.748395000000755,39.791292000006706,1.0,hover_text_char_count,Hover length,420.0,0.0,True,0 -benchmark,pyright,ty,Ty,web,,edit response then hover (edit+hover),textDocument/hover,textDocument/hover,True,2.945125800005144,2.9775828000083493,1.0,hover_text_char_count,Hover length,1555.0,1135.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,web,,edit response then hover (edit+hover),textDocument/hover,textDocument/hover,True,3.073950400010972,5.503030000016906,1.0,hover_text_char_count,Hover length,9977.0,9557.0,True,0 -benchmark,pyright,pylsp-mypy,pylsp-mypy,web,,edit response then hover (edit+hover),textDocument/hover,textDocument/hover,True,228.47313319999785,230.59836840000116,1.0,hover_text_char_count,Hover length,880.0,460.0,True,0 -benchmark,pyright,pyrefly,Pyrefly,tsp_core,,builtins semantic tokens,typeServer/semanticTokens,semantic token impl using typeServer/getComputedType,True,1.854982400004701,4.392242600005146,1.0,semantic_token_count,Semantic tokens found,30.0,,True,0 -benchmark,pyright,pyrefly,Pyrefly,tsp_core,,builtin int computed type,typeServer/getComputedType,typeServer/getComputedType,True,0.2136249999921347,0.21787519999065807,1.0,top_level_count,Results found,7.0,,True,0 -benchmark,pyright,pyrefly,Pyrefly,tsp_core,,list declared type,typeServer/getDeclaredType,typeServer/getDeclaredType,True,0.23785760000123446,0.24708740000392027,1.0,top_level_count,Results found,7.0,,True,0 -benchmark,pyright,pyrefly,Pyrefly,tsp_core,,generic specialization computed type,typeServer/getComputedType,typeServer/getComputedType,True,0.24949920000381098,0.2622378000012304,1.0,top_level_count,Results found,7.0,,True,0 -benchmark,pyright,pyrefly,Pyrefly,tsp_core,,flow narrowed branch type,typeServer/getComputedType,typeServer/getComputedType,False,0.20384859999467153,0.22056599999018545,,,,,,False,10 -benchmark,pyright,pyrefly,Pyrefly,tsp_core,,stdlib path computed type,typeServer/getComputedType,typeServer/getComputedType,True,0.20946899999785273,0.2118409999980031,1.0,top_level_count,Results found,7.0,,True,0 -benchmark,pyright,pyrefly,Pyrefly,tsp_core,,function argument expected type,typeServer/getExpectedType,typeServer/getExpectedType,True,0.25122760000044764,0.2687614000024041,1.0,top_level_count,Results found,7.0,,True,0 -benchmark,pyright,pyrefly,Pyrefly,tsp_core,,edited narrowing recomputes type (edit+getComputedType),typeServer/getComputedType,typeServer/getComputedType,False,0.21575399998710054,0.2328337999813357,,,,,,False,10 -benchmark,pyright,pyrefly,Pyrefly,tsp_semantic,,django semantic tokens,typeServer/semanticTokens,semantic token impl using typeServer/getComputedType,True,17.647472799995967,36.638987200007016,1.0,semantic_token_count,Semantic tokens found,126.0,,True,0 -benchmark,pyright,pyrefly,Pyrefly,tsp_semantic,,transformers semantic tokens,typeServer/semanticTokens,semantic token impl using typeServer/getComputedType,True,122.07399220000639,150.7368873999951,1.0,semantic_token_count,Semantic tokens found,74.0,,True,0 -benchmark,pyright,pyrefly,Pyrefly,tsp_semantic,,stdlib semantic tokens,typeServer/semanticTokens,semantic token impl using typeServer/getComputedType,True,59.963732800002845,73.58896840000853,1.0,semantic_token_count,Semantic tokens found,75.0,,True,0 diff --git a/latest-results/summary-20260929T060553Z.csv b/latest-results/summary-20260929T060553Z.csv new file mode 100644 index 0000000..3b110d8 --- /dev/null +++ b/latest-results/summary-20260929T060553Z.csv @@ -0,0 +1,132 @@ +report_type,baseline_server_id,server_id,server_name,suite_name,scenario_name,point_label,method,method_display,success,mean_ms,p95_ms,non_empty_rate,result_metric_name,result_metric_label,result_metric_value,result_metric_delta,validation_passed,validation_failure_count +benchmark,pyright,pyright,Pyright,data_science,,dataframe completion,textDocument/completion,textDocument/completion,True,4.1516369999953895,6.445084399990719,1.0,completion_item_count,Completions found,201.0,0.0,True,0 +benchmark,pyright,ty,Ty,data_science,,dataframe completion,textDocument/completion,textDocument/completion,True,1.2833722000095804,1.4152822000141896,1.0,completion_item_count,Completions found,223.0,22.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,data_science,,dataframe completion,textDocument/completion,textDocument/completion,True,69.13779699998486,272.09671279995285,1.0,completion_item_count,Completions found,250.0,49.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,data_science,,dataframe completion,textDocument/completion,textDocument/completion,True,165.83094220002295,393.6629760000414,1.0,completion_item_count,Completions found,188.0,-13.0,True,0 +benchmark,pyright,pyright,Pyright,data_science,,dataframe describe hover,textDocument/hover,textDocument/hover,True,0.9445098000128382,1.3326326000196786,1.0,hover_text_char_count,Hover length,4019.0,0.0,True,0 +benchmark,pyright,ty,Ty,data_science,,dataframe describe hover,textDocument/hover,textDocument/hover,True,0.22679279998101265,0.2440645999968183,1.0,hover_text_char_count,Hover length,4232.0,213.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,data_science,,dataframe describe hover,textDocument/hover,textDocument/hover,True,1.8241021999756413,1.9415685999774723,1.0,hover_text_char_count,Hover length,3182.0,-837.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,data_science,,dataframe describe hover,textDocument/hover,textDocument/hover,True,151.47411399998418,153.1753617999584,1.0,hover_text_char_count,Hover length,4134.0,115.0,True,0 +benchmark,pyright,pyright,Pyright,data_science,,summarize definition,textDocument/definition,textDocument/definition,True,0.31352699999160905,0.37325919996646917,1.0,location_count,Definitions found,1.0,0.0,True,0 +benchmark,pyright,ty,Ty,data_science,,summarize definition,textDocument/definition,textDocument/definition,True,0.1453183999956309,0.16398239997670316,1.0,location_count,Definitions found,1.0,0.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,data_science,,summarize definition,textDocument/definition,textDocument/definition,True,0.21505699999124772,0.2761139999847728,1.0,location_count,Definitions found,1.0,0.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,data_science,,summarize definition,textDocument/definition,textDocument/definition,True,0.7670243999882587,0.8180431999790017,1.0,location_count,Definitions found,1.0,0.0,True,0 +benchmark,pyright,pyright,Pyright,data_science,,edit array then complete (edit+completion),textDocument/completion,textDocument/completion,True,227.49116660000936,336.92690040001025,1.0,completion_item_count,Completions found,168.0,0.0,True,0 +benchmark,pyright,ty,Ty,data_science,,edit array then complete (edit+completion),textDocument/completion,textDocument/completion,True,9.326976600027592,10.832260400047744,1.0,completion_item_count,Completions found,168.0,0.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,data_science,,edit array then complete (edit+completion),textDocument/completion,textDocument/completion,True,14.511469800027044,17.238983200013536,1.0,completion_item_count,Completions found,149.0,-19.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,data_science,,edit array then complete (edit+completion),textDocument/completion,textDocument/completion,False,3.3870583999942028,3.542692600058217,0.0,completion_item_count,Completions found,0.0,-168.0,False,10 +benchmark,pyright,pyright,Pyright,data_science,,edit array then hover (edit+hover),textDocument/hover,textDocument/hover,True,22.635305599987987,23.84897919997684,1.0,hover_text_char_count,Hover length,278.0,0.0,True,0 +benchmark,pyright,ty,Ty,data_science,,edit array then hover (edit+hover),textDocument/hover,textDocument/hover,True,4.165223399968454,4.191712999954689,1.0,hover_text_char_count,Hover length,267.0,-11.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,data_science,,edit array then hover (edit+hover),textDocument/hover,textDocument/hover,True,0.4426488000262907,0.6969828000364942,1.0,hover_text_char_count,Hover length,2546.0,2268.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,data_science,,edit array then hover (edit+hover),textDocument/hover,textDocument/hover,True,139.99701600002936,147.2010886000362,1.0,hover_text_char_count,Hover length,5662.0,5384.0,True,0 +benchmark,pyright,pyright,Pyright,django,,queryset completion,textDocument/completion,textDocument/completion,True,3.8023774000066624,6.128928199996153,1.0,completion_item_count,Completions found,10.0,0.0,True,0 +benchmark,pyright,ty,Ty,django,,queryset completion,textDocument/completion,textDocument/completion,True,3.6815590000060183,5.805512000029012,1.0,completion_item_count,Completions found,261.0,251.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,django,,queryset completion,textDocument/completion,textDocument/completion,True,11.74937640000735,45.40501660001154,1.0,completion_item_count,Completions found,15.0,5.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,django,,queryset completion,textDocument/completion,textDocument/completion,True,162.20259679996616,508.27810899997974,1.0,completion_item_count,Completions found,2.0,-8.0,True,0 +benchmark,pyright,pyright,Pyright,django,,queryset filter hover,textDocument/hover,textDocument/hover,True,0.40895220000720656,0.4637406000256305,1.0,hover_text_char_count,Hover length,57.0,0.0,True,0 +benchmark,pyright,ty,Ty,django,,queryset filter hover,textDocument/hover,textDocument/hover,True,0.15894860000571498,0.1833996000186744,1.0,hover_text_char_count,Hover length,46.0,-11.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,django,,queryset filter hover,textDocument/hover,textDocument/hover,True,0.19548760001271148,0.20602560005045234,1.0,hover_text_char_count,Hover length,298.0,241.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,django,,queryset filter hover,textDocument/hover,textDocument/hover,True,128.23753020002187,128.8983670000789,1.0,hover_text_char_count,Hover length,57.0,0.0,True,0 +benchmark,pyright,pyright,Pyright,django,,model definition,textDocument/definition,textDocument/definition,True,0.2996458000097846,0.34013219999451394,1.0,location_count,Definitions found,1.0,0.0,True,0 +benchmark,pyright,ty,Ty,django,,model definition,textDocument/definition,textDocument/definition,True,0.12742360001993802,0.1336209999408311,1.0,location_count,Definitions found,1.0,0.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,django,,model definition,textDocument/definition,textDocument/definition,True,0.15809980000085488,0.17261699997561664,1.0,location_count,Definitions found,1.0,0.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,django,,model definition,textDocument/definition,textDocument/definition,True,0.7466138000381761,0.7795170000463258,1.0,location_count,Definitions found,1.0,0.0,True,0 +benchmark,pyright,pyright,Pyright,django,,edit queryset then complete (edit+completion),textDocument/completion,textDocument/completion,True,20.26884040000141,21.67124040000772,1.0,completion_item_count,Completions found,104.0,0.0,True,0 +benchmark,pyright,ty,Ty,django,,edit queryset then complete (edit+completion),textDocument/completion,textDocument/completion,True,3.5577389999843945,3.7055331999681584,1.0,completion_item_count,Completions found,104.0,0.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,django,,edit queryset then complete (edit+completion),textDocument/completion,textDocument/completion,True,1.48782520000168,1.5693471999838948,1.0,completion_item_count,Completions found,83.0,-21.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,django,,edit queryset then complete (edit+completion),textDocument/completion,textDocument/completion,True,189.0809881999985,228.1125388000191,1.0,completion_item_count,Completions found,143.0,39.0,True,0 +benchmark,pyright,pyright,Pyright,django,,edit queryset then hover (edit+hover),textDocument/hover,textDocument/hover,True,30.201907799994387,35.72295239998766,1.0,hover_text_char_count,Hover length,83.0,0.0,True,0 +benchmark,pyright,ty,Ty,django,,edit queryset then hover (edit+hover),textDocument/hover,textDocument/hover,True,2.3955969999860827,2.4120317999404506,1.0,hover_text_char_count,Hover length,100.0,17.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,django,,edit queryset then hover (edit+hover),textDocument/hover,textDocument/hover,True,0.6083821999936845,0.625213799935409,1.0,hover_text_char_count,Hover length,858.0,775.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,django,,edit queryset then hover (edit+hover),textDocument/hover,textDocument/hover,True,192.22229659999357,194.26925159998518,1.0,hover_text_char_count,Hover length,71.0,-12.0,True,0 +benchmark,pyright,pyright,Pyright,pandas,,report dataframe completion,textDocument/completion,textDocument/completion,True,57.90496560001657,196.84774620000093,1.0,completion_item_count,Completions found,271.2,0.0,True,0 +benchmark,pyright,ty,Ty,pandas,,report dataframe completion,textDocument/completion,textDocument/completion,True,14.112089399964134,17.17059679999693,1.0,completion_item_count,Completions found,1000.0,728.8,True,0 +benchmark,pyright,pyrefly,Pyrefly,pandas,,report dataframe completion,textDocument/completion,textDocument/completion,True,64.18918100002884,256.12248260003975,1.0,completion_item_count,Completions found,16.0,-255.2,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,pandas,,report dataframe completion,textDocument/completion,textDocument/completion,True,45.97375740002008,69.02264699997431,1.0,completion_item_count,Completions found,6.0,-265.2,True,0 +benchmark,pyright,pyright,Pyright,pandas,,dataframe groupby hover,textDocument/hover,textDocument/hover,True,0.5679734000068493,0.6286124000439486,1.0,hover_text_char_count,Hover length,350.0,0.0,True,0 +benchmark,pyright,ty,Ty,pandas,,dataframe groupby hover,textDocument/hover,textDocument/hover,True,0.20130659997903422,0.23197819998586056,1.0,hover_text_char_count,Hover length,329.0,-21.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,pandas,,dataframe groupby hover,textDocument/hover,textDocument/hover,True,4.07564659997206,7.342648599978928,1.0,hover_text_char_count,Hover length,2759.0,2409.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,pandas,,dataframe groupby hover,textDocument/hover,textDocument/hover,True,157.65772499999002,159.47941980002724,1.0,hover_text_char_count,Hover length,301.0,-49.0,True,0 +benchmark,pyright,pyright,Pyright,pandas,,build report definition,textDocument/definition,textDocument/definition,True,0.3287115999910384,0.46738599997979685,1.0,location_count,Definitions found,1.0,0.0,True,0 +benchmark,pyright,ty,Ty,pandas,,build report definition,textDocument/definition,textDocument/definition,True,0.14213439997092792,0.15125539996461157,1.0,location_count,Definitions found,1.0,0.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,pandas,,build report definition,textDocument/definition,textDocument/definition,True,3.528414600009455,5.35750060000737,1.0,location_count,Definitions found,1.0,0.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,pandas,,build report definition,textDocument/definition,textDocument/definition,True,0.8001959999774044,0.8185900000171387,1.0,location_count,Definitions found,1.0,0.0,True,0 +benchmark,pyright,pyright,Pyright,pandas,,edit dataframe then complete (edit+completion),textDocument/completion,textDocument/completion,True,363.89463080000723,832.4558429999795,1.0,completion_item_count,Completions found,440.0,0.0,True,0 +benchmark,pyright,ty,Ty,pandas,,edit dataframe then complete (edit+completion),textDocument/completion,textDocument/completion,True,13.319288200023038,14.488761200004774,1.0,completion_item_count,Completions found,448.0,8.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,pandas,,edit dataframe then complete (edit+completion),textDocument/completion,textDocument/completion,True,27.387597200026903,34.46320100006233,1.0,completion_item_count,Completions found,256.0,-184.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,pandas,,edit dataframe then complete (edit+completion),textDocument/completion,textDocument/completion,True,182.54443200000878,187.6561234000519,1.0,completion_item_count,Completions found,441.0,1.0,True,0 +benchmark,pyright,pyright,Pyright,pandas,,edit dataframe then hover (edit+hover),textDocument/hover,textDocument/hover,True,8.221072399987861,10.268686599988541,1.0,hover_text_char_count,Hover length,4292.0,0.0,True,0 +benchmark,pyright,ty,Ty,pandas,,edit dataframe then hover (edit+hover),textDocument/hover,textDocument/hover,True,3.4300991999771213,3.456331199959095,1.0,hover_text_char_count,Hover length,4441.0,149.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,pandas,,edit dataframe then hover (edit+hover),textDocument/hover,textDocument/hover,True,7.441802600033043,10.207173400044667,1.0,hover_text_char_count,Hover length,943.0,-3349.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,pandas,,edit dataframe then hover (edit+hover),textDocument/hover,textDocument/hover,True,147.4257257999625,150.63052639998205,1.0,hover_text_char_count,Hover length,232.0,-4060.0,True,0 +benchmark,pyright,pyright,Pyright,sqlalchemy,,query completion,textDocument/completion,textDocument/completion,True,15.678024799990453,20.852941600014674,1.0,completion_item_count,Completions found,1.0,0.0,True,0 +benchmark,pyright,ty,Ty,sqlalchemy,,query completion,textDocument/completion,textDocument/completion,True,2.5991027999680227,6.004977799989319,1.0,completion_item_count,Completions found,1.0,0.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,sqlalchemy,,query completion,textDocument/completion,textDocument/completion,True,64.92233619999297,259.0805628000225,1.0,completion_item_count,Completions found,15.0,14.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,sqlalchemy,,query completion,textDocument/completion,textDocument/completion,True,153.23857420000877,420.8585562000052,1.0,completion_item_count,Completions found,1.0,0.0,True,0 +benchmark,pyright,pyright,Pyright,sqlalchemy,,sessionmaker hover,textDocument/hover,textDocument/hover,True,1.5498506000312773,2.108816600070895,1.0,hover_text_char_count,Hover length,10579.0,0.0,True,0 +benchmark,pyright,ty,Ty,sqlalchemy,,sessionmaker hover,textDocument/hover,textDocument/hover,True,0.2953940000224975,0.31093940001483134,1.0,hover_text_char_count,Hover length,10621.0,42.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,sqlalchemy,,sessionmaker hover,textDocument/hover,textDocument/hover,True,2.9175551999969684,3.4361864000175046,1.0,hover_text_char_count,Hover length,15239.0,4660.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,sqlalchemy,,sessionmaker hover,textDocument/hover,textDocument/hover,True,248.87122359998557,253.96283760001097,1.0,hover_text_char_count,Hover length,10498.0,-81.0,True,0 +benchmark,pyright,pyright,Pyright,sqlalchemy,,mapped class definition,textDocument/definition,textDocument/definition,True,0.33511960000396357,0.4037109999671884,1.0,location_count,Definitions found,1.0,0.0,True,0 +benchmark,pyright,ty,Ty,sqlalchemy,,mapped class definition,textDocument/definition,textDocument/definition,True,0.1406573999702232,0.16206379998493503,1.0,location_count,Definitions found,2.0,1.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,sqlalchemy,,mapped class definition,textDocument/definition,textDocument/definition,True,2.7347121999810042,2.938994599958278,1.0,location_count,Definitions found,1.0,0.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,sqlalchemy,,mapped class definition,textDocument/definition,textDocument/definition,True,0.832430800005568,0.9406076000459506,1.0,location_count,Definitions found,1.0,0.0,True,0 +benchmark,pyright,pyright,Pyright,sqlalchemy,,edit query then complete (edit+completion),textDocument/completion,textDocument/completion,True,107.69080460004261,134.28705379999428,1.0,completion_item_count,Completions found,38.0,0.0,True,0 +benchmark,pyright,ty,Ty,sqlalchemy,,edit query then complete (edit+completion),textDocument/completion,textDocument/completion,True,4.095232199983911,4.506790999926125,1.0,completion_item_count,Completions found,23.0,-15.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,sqlalchemy,,edit query then complete (edit+completion),textDocument/completion,textDocument/completion,True,9.43841500002236,23.85389220005436,1.0,completion_item_count,Completions found,17.0,-21.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,sqlalchemy,,edit query then complete (edit+completion),textDocument/completion,textDocument/completion,False,29.667597399998158,29.99561779997748,0.0,completion_item_count,Completions found,0.0,-38.0,False,10 +benchmark,pyright,pyright,Pyright,sqlalchemy,,edit session then hover (edit+hover),textDocument/hover,textDocument/hover,True,74.3290173999867,81.44138880002174,1.0,hover_text_char_count,Hover length,893.0,0.0,True,0 +benchmark,pyright,ty,Ty,sqlalchemy,,edit session then hover (edit+hover),textDocument/hover,textDocument/hover,True,3.7862604000110878,3.838156799997705,1.0,hover_text_char_count,Hover length,951.0,58.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,sqlalchemy,,edit session then hover (edit+hover),textDocument/hover,textDocument/hover,True,8.33640339997146,23.58592579996639,1.0,hover_text_char_count,Hover length,2242.0,1349.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,sqlalchemy,,edit session then hover (edit+hover),textDocument/hover,textDocument/hover,False,29.532160000007934,30.424360600045475,0.0,hover_text_char_count,Hover length,0.0,-893.0,False,10 +benchmark,pyright,pyright,Pyright,transformers,,classifier pipeline completion,textDocument/completion,textDocument/completion,True,46.54004599997279,70.05506279999736,1.0,completion_item_count,Completions found,123.0,0.0,True,0 +benchmark,pyright,ty,Ty,transformers,,classifier pipeline completion,textDocument/completion,textDocument/completion,True,11.055732800036822,12.120645000004515,1.0,completion_item_count,Completions found,777.0,654.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,transformers,,classifier pipeline completion,textDocument/completion,textDocument/completion,True,591.0293486,2363.4758575999963,1.0,completion_item_count,Completions found,15.0,-108.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,transformers,,classifier pipeline completion,textDocument/completion,textDocument/completion,True,102.32577260001108,104.36641300000247,1.0,completion_item_count,Completions found,2.0,-121.0,True,0 +benchmark,pyright,pyright,Pyright,transformers,,pipeline hover,textDocument/hover,textDocument/hover,True,0.38403279997965,0.4567578000205685,1.0,hover_text_char_count,Hover length,34.0,0.0,True,0 +benchmark,pyright,ty,Ty,transformers,,pipeline hover,textDocument/hover,textDocument/hover,True,0.14895060000981175,0.159978800047611,1.0,hover_text_char_count,Hover length,7.0,-27.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,transformers,,pipeline hover,textDocument/hover,textDocument/hover,True,0.13909300000705116,0.16989319999538566,1.0,hover_text_char_count,Hover length,48.0,14.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,transformers,,pipeline hover,textDocument/hover,textDocument/hover,False,2152.5753828000006,2240.4952184000194,0.0,hover_text_char_count,Hover length,0.0,-34.0,False,10 +benchmark,pyright,pyright,Pyright,transformers,,auto tokenizer definition,textDocument/definition,textDocument/definition,True,0.3263941999875897,0.3459960000554929,1.0,location_count,Definitions found,1.0,0.0,True,0 +benchmark,pyright,ty,Ty,transformers,,auto tokenizer definition,textDocument/definition,textDocument/definition,True,0.17734440000367613,0.1910905999920942,1.0,location_count,Definitions found,1.0,0.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,transformers,,auto tokenizer definition,textDocument/definition,textDocument/definition,True,0.14463519996752439,0.16733959994326142,1.0,location_count,Definitions found,1.0,0.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,transformers,,auto tokenizer definition,textDocument/definition,textDocument/definition,True,1731.7756821999865,1781.614458799936,1.0,location_count,Definitions found,1.0,0.0,True,0 +benchmark,pyright,pyright,Pyright,transformers,,edit prediction then complete (edit+completion),textDocument/completion,textDocument/completion,True,5.199794200007091,6.649819400013257,0.0,completion_item_count,Completions found,0.0,0.0,True,0 +benchmark,pyright,ty,Ty,transformers,,edit prediction then complete (edit+completion),textDocument/completion,textDocument/completion,True,6.237762599994312,8.567935200017018,1.0,completion_item_count,Completions found,23.0,23.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,transformers,,edit prediction then complete (edit+completion),textDocument/completion,textDocument/completion,True,2.1167502000480454,7.6090176000661796,0.0,completion_item_count,Completions found,0.0,0.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,transformers,,edit prediction then complete (edit+completion),textDocument/completion,textDocument/completion,True,2.1063165999976263,2.353283599950373,0.0,completion_item_count,Completions found,0.0,0.0,True,0 +benchmark,pyright,pyright,Pyright,transformers,,edit tokenizer then hover (edit+hover),textDocument/hover,textDocument/hover,True,382.31186419998267,397.66792539996914,1.0,hover_text_char_count,Hover length,30.0,0.0,True,0 +benchmark,pyright,ty,Ty,transformers,,edit tokenizer then hover (edit+hover),textDocument/hover,textDocument/hover,True,5.750827199995001,6.641188800017517,1.0,hover_text_char_count,Hover length,7.0,-23.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,transformers,,edit tokenizer then hover (edit+hover),textDocument/hover,textDocument/hover,True,6.116003999954955,13.948519999962626,1.0,hover_text_char_count,Hover length,33.0,3.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,transformers,,edit tokenizer then hover (edit+hover),textDocument/hover,textDocument/hover,False,2011.858195800005,2032.5817864000328,0.0,hover_text_char_count,Hover length,0.0,-30.0,False,10 +benchmark,pyright,pyright,Pyright,web,,request args completion,textDocument/completion,textDocument/completion,True,3.6087432000158515,6.605264400013766,1.0,completion_item_count,Completions found,14.0,0.0,True,0 +benchmark,pyright,ty,Ty,web,,request args completion,textDocument/completion,textDocument/completion,True,5.26830040000732,7.372259000021585,1.0,completion_item_count,Completions found,467.0,453.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,web,,request args completion,textDocument/completion,textDocument/completion,True,41.28426560002936,134.2596840000169,1.0,completion_item_count,Completions found,487.8,473.8,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,web,,request args completion,textDocument/completion,textDocument/completion,True,59.362499400026536,123.68598780005865,1.0,completion_item_count,Completions found,1.0,-13.0,True,0 +benchmark,pyright,pyright,Pyright,web,,client session hover,textDocument/hover,textDocument/hover,True,0.391826600002787,0.45116800001778756,1.0,hover_text_char_count,Hover length,26.0,0.0,True,0 +benchmark,pyright,ty,Ty,web,,client session hover,textDocument/hover,textDocument/hover,True,0.13441200001125253,0.15350460000718158,1.0,hover_text_char_count,Hover length,7.0,-19.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,web,,client session hover,textDocument/hover,textDocument/hover,True,4.973439400009738,17.705702600005676,1.0,hover_text_char_count,Hover length,167.0,141.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,web,,client session hover,textDocument/hover,textDocument/hover,True,99.52645860000757,306.6159084000446,1.0,hover_text_char_count,Hover length,359.0,333.0,True,0 +benchmark,pyright,pyright,Pyright,web,,client references,textDocument/references,textDocument/references,True,1.0460473999955866,2.5138970000170953,1.0,location_count,References found,2.0,0.0,True,0 +benchmark,pyright,ty,Ty,web,,client references,textDocument/references,textDocument/references,True,0.465901000006852,0.557774199933192,1.0,location_count,References found,2.0,0.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,web,,client references,textDocument/references,textDocument/references,True,0.24867760000688577,0.28504420001809194,1.0,location_count,References found,2.0,0.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,web,,client references,textDocument/references,textDocument/references,True,3.015231599965773,3.9973895999537485,1.0,location_count,References found,2.0,0.0,True,0 +benchmark,pyright,pyright,Pyright,web,,edit response then complete (edit+completion),textDocument/completion,textDocument/completion,True,3.9076627999975244,5.128875400009747,1.0,completion_item_count,Completions found,205.0,0.0,True,0 +benchmark,pyright,ty,Ty,web,,edit response then complete (edit+completion),textDocument/completion,textDocument/completion,True,3.822169000022768,4.429759000026934,1.0,completion_item_count,Completions found,225.0,20.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,web,,edit response then complete (edit+completion),textDocument/completion,textDocument/completion,True,0.4237383999679878,0.4444231999286785,1.0,completion_item_count,Completions found,32.0,-173.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,web,,edit response then complete (edit+completion),textDocument/completion,textDocument/completion,True,62.76562939997348,64.2273995999858,1.0,completion_item_count,Completions found,57.0,-148.0,True,0 +benchmark,pyright,pyright,Pyright,web,,edit response then hover (edit+hover),textDocument/hover,textDocument/hover,True,26.10197219996735,29.43189579996215,1.0,hover_text_char_count,Hover length,420.0,0.0,True,0 +benchmark,pyright,ty,Ty,web,,edit response then hover (edit+hover),textDocument/hover,textDocument/hover,True,3.141586599963375,3.6147573999642186,1.0,hover_text_char_count,Hover length,1555.0,1135.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,web,,edit response then hover (edit+hover),textDocument/hover,textDocument/hover,True,3.0832576000193512,4.686702199978754,1.0,hover_text_char_count,Hover length,9977.0,9557.0,True,0 +benchmark,pyright,pylsp-mypy,pylsp-mypy,web,,edit response then hover (edit+hover),textDocument/hover,textDocument/hover,True,186.40655980000247,188.66251320005176,1.0,hover_text_char_count,Hover length,880.0,460.0,True,0 +benchmark,pyright,pyrefly,Pyrefly,tsp_core,,builtins semantic tokens,typeServer/semanticTokens,semantic token impl using typeServer/getComputedType,True,0.7396289999860528,0.7767429999603337,1.0,semantic_token_count,Semantic tokens found,30.0,,True,0 +benchmark,pyright,pyrefly,Pyrefly,tsp_core,,builtin int computed type,typeServer/getComputedType,typeServer/getComputedType,True,0.13460999998642365,0.13637659997129958,1.0,top_level_count,Results found,7.0,,True,0 +benchmark,pyright,pyrefly,Pyrefly,tsp_core,,list declared type,typeServer/getDeclaredType,typeServer/getDeclaredType,True,0.16378800000893534,0.1673093999897901,1.0,top_level_count,Results found,7.0,,True,0 +benchmark,pyright,pyrefly,Pyrefly,tsp_core,,generic specialization computed type,typeServer/getComputedType,typeServer/getComputedType,True,0.15763660001084645,0.1755817999764986,1.0,top_level_count,Results found,7.0,,True,0 +benchmark,pyright,pyrefly,Pyrefly,tsp_core,,flow narrowed branch type,typeServer/getComputedType,typeServer/getComputedType,False,0.1436676000139414,0.1536348000399812,,,,,,False,10 +benchmark,pyright,pyrefly,Pyrefly,tsp_core,,stdlib path computed type,typeServer/getComputedType,typeServer/getComputedType,True,0.156366800001706,0.18540819999088853,1.0,top_level_count,Results found,7.0,,True,0 +benchmark,pyright,pyrefly,Pyrefly,tsp_core,,function argument expected type,typeServer/getExpectedType,typeServer/getExpectedType,True,0.1468966000174987,0.1560990000371021,1.0,top_level_count,Results found,7.0,,True,0 +benchmark,pyright,pyrefly,Pyrefly,tsp_core,,edited narrowing recomputes type (edit+getComputedType),typeServer/getComputedType,typeServer/getComputedType,False,0.15726740002719453,0.17344040006719297,,,,,,False,10 +benchmark,pyright,pyrefly,Pyrefly,tsp_semantic,,django semantic tokens,typeServer/semanticTokens,semantic token impl using typeServer/getComputedType,True,9.570796000002701,18.498301799991165,1.0,semantic_token_count,Semantic tokens found,126.0,,True,0 +benchmark,pyright,pyrefly,Pyrefly,tsp_semantic,,transformers semantic tokens,typeServer/semanticTokens,semantic token impl using typeServer/getComputedType,True,45.62198780001836,47.40470619999542,1.0,semantic_token_count,Semantic tokens found,74.0,,True,0 +benchmark,pyright,pyrefly,Pyrefly,tsp_semantic,,stdlib semantic tokens,typeServer/semanticTokens,semantic token impl using typeServer/getComputedType,True,9.122424400015916,21.592673800000735,1.0,semantic_token_count,Semantic tokens found,75.0,,True,0 diff --git a/latest-results/summary-20260928T060730Z.json b/latest-results/summary-20260929T060553Z.json similarity index 93% rename from latest-results/summary-20260928T060730Z.json rename to latest-results/summary-20260929T060553Z.json index 42d8ecc..0b5da1c 100644 --- a/latest-results/summary-20260928T060730Z.json +++ b/latest-results/summary-20260929T060553Z.json @@ -17,12 +17,12 @@ "tsp_semantic" ], "baseline_server": "pyright", - "generated_at": "20260928T060730Z", + "generated_at": "20260929T060553Z", "servers": [ { "id": "pyright", "display_name": "Pyright", - "output_path": "results/bench-servers/pyright-20260928T060730Z.json", + "output_path": "results/bench-servers/pyright-20260929T060553Z.json", "success": true, "benchmark_count": 6, "command": [ @@ -58,7 +58,7 @@ { "id": "ty", "display_name": "Ty", - "output_path": "results/bench-servers/ty-20260928T060730Z.json", + "output_path": "results/bench-servers/ty-20260929T060553Z.json", "success": true, "benchmark_count": 6, "command": [ @@ -91,7 +91,7 @@ { "id": "pyrefly", "display_name": "Pyrefly", - "output_path": "results/bench-servers/pyrefly-20260928T060730Z.json", + "output_path": "results/bench-servers/pyrefly-20260929T060553Z.json", "success": false, "benchmark_count": 8, "command": [ @@ -104,7 +104,7 @@ "source_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/.python-lsp-compare/servers/pyrefly/venv/bin/pyrefly", "version": { "kind": "release", - "label": "1.3.1", + "label": "1.3.2", "repo_root": null, "commit": null, "short_commit": null, @@ -131,7 +131,7 @@ { "id": "pylsp-mypy", "display_name": "pylsp-mypy", - "output_path": "results/bench-servers/pylsp-mypy-20260928T060730Z.json", + "output_path": "results/bench-servers/pylsp-mypy-20260929T060553Z.json", "success": false, "benchmark_count": 6, "command": [ diff --git a/latest-results/summary-20260928T060730Z.md b/latest-results/summary-20260929T060553Z.md similarity index 60% rename from latest-results/summary-20260928T060730Z.md rename to latest-results/summary-20260929T060553Z.md index 3e5f319..f58baf0 100644 --- a/latest-results/summary-20260928T060730Z.md +++ b/latest-results/summary-20260929T060553Z.md @@ -1,8 +1,8 @@ # Python LSP Benchmark Comparison -Generated from `results/bench-servers/summary-20260928T060730Z.json` +Generated from `results/bench-servers/summary-20260929T060553Z.json` -- Generated at: 20260928T060730Z +- Generated at: 20260929T060553Z - Config: `github-releases` - Servers: pyright, ty, pyrefly, pylsp-mypy - Baseline server: Pyright (pyright) @@ -14,7 +14,7 @@ Generated from `results/bench-servers/summary-20260928T060730Z.json` | --- | --- | --- | | Pyright | 1.1.414 | /home/runner/work/python-lsp-compare/python-lsp-compare/.python-lsp-compare/servers/pyright/1.1.414/package/dist/pyright-langserver.js | | Ty | 0.0.84 | /home/runner/work/python-lsp-compare/python-lsp-compare/.python-lsp-compare/servers/ty/0.0.84/ty-x86_64-unknown-linux-gnu/ty | -| Pyrefly | 1.3.1 | /home/runner/work/python-lsp-compare/python-lsp-compare/.python-lsp-compare/servers/pyrefly/venv/bin/pyrefly | +| Pyrefly | 1.3.2 | /home/runner/work/python-lsp-compare/python-lsp-compare/.python-lsp-compare/servers/pyrefly/venv/bin/pyrefly | | pylsp-mypy | 1.15.0 | /home/runner/work/python-lsp-compare/python-lsp-compare/.python-lsp-compare/servers/pylsp-mypy/venv/bin/pylsp | ## Server Notes @@ -30,10 +30,10 @@ Generated from `results/bench-servers/summary-20260928T060730Z.json` | Server | Success | Benchmarks | Wall clock ms | Avg measured ms | Measured requests | Non-empty % | Failed points | | --- | --- | --- | ---: | ---: | ---: | ---: | ---: | -| Ty | yes | 6 | 5088.02 | 4.16 | 150 | 100% | 0 | -| Pyrefly | no | 8 | 16436.19 | 37.77 | 205 | 97% | 2 | -| Pyright | yes | 6 | 38514.36 | 74.14 | 150 | 97% | 0 | -| pylsp-mypy | no | 6 | 217566.56 | 364.51 | 150 | 80% | 5 | +| Ty | yes | 6 | 4105.65 | 3.45 | 150 | 100% | 0 | +| Pyrefly | no | 8 | 11731.81 | 24.66 | 205 | 97% | 2 | +| Pyright | yes | 6 | 28362.29 | 47.03 | 150 | 97% | 0 | +| pylsp-mypy | no | 6 | 165606.66 | 284.74 | 150 | 80% | 5 | *Wall clock ms includes server startup, warmup iterations, and shutdown — but excludes one-time environment creation and dependency installation.* @@ -41,10 +41,10 @@ Generated from `results/bench-servers/summary-20260928T060730Z.json` | Server | Success | Wall clock ms | Avg measured ms | Points | Measured requests | Non-empty % | Failed points | | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | -| Ty | yes | 504.17 | 4.09 | 5 | 25 | 100% | 0 | -| Pyrefly | yes | 1053.90 | 22.73 | 5 | 25 | 100% | 0 | -| Pyright | yes | 4500.42 | 73.97 | 5 | 25 | 100% | 0 | -| pylsp-mypy | no | 7688.41 | 114.99 | 5 | 25 | 80% | 1 | +| Ty | yes | 390.23 | 3.03 | 5 | 25 | 100% | 0 | +| Pyrefly | yes | 819.48 | 17.23 | 5 | 25 | 100% | 0 | +| Pyright | yes | 3302.06 | 51.11 | 5 | 25 | 100% | 0 | +| pylsp-mypy | no | 6331.52 | 92.29 | 5 | 25 | 80% | 1 | ### dataframe completion @@ -52,10 +52,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Ty | yes | 1.65 | 1.89 | 100% | 223.00 | +22.00 | pass | -| Pyright | yes | 6.12 | 8.84 | 100% | 201.00 | 0.00 | pass | -| Pyrefly | yes | 83.23 | 329.24 | 100% | 250.00 | +49.00 | pass | -| pylsp-mypy | yes | 227.50 | 468.36 | 100% | 188.00 | -13.00 | pass | +| Ty | yes | 1.28 | 1.42 | 100% | 223.00 | +22.00 | pass | +| Pyright | yes | 4.15 | 6.45 | 100% | 201.00 | 0.00 | pass | +| Pyrefly | yes | 69.14 | 272.10 | 100% | 250.00 | +49.00 | pass | +| pylsp-mypy | yes | 165.83 | 393.66 | 100% | 188.00 | -13.00 | pass | ### dataframe describe hover @@ -63,10 +63,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Ty | yes | 0.33 | 0.36 | 100% | 4232.00 | +213.00 | pass | -| Pyright | yes | 1.16 | 1.49 | 100% | 4019.00 | 0.00 | pass | -| Pyrefly | yes | 2.23 | 2.33 | 100% | 3182.00 | -837.00 | pass | -| pylsp-mypy | yes | 175.42 | 175.90 | 100% | 4134.00 | +115.00 | pass | +| Ty | yes | 0.23 | 0.24 | 100% | 4232.00 | +213.00 | pass | +| Pyright | yes | 0.94 | 1.33 | 100% | 4019.00 | 0.00 | pass | +| Pyrefly | yes | 1.82 | 1.94 | 100% | 3182.00 | -837.00 | pass | +| pylsp-mypy | yes | 151.47 | 153.18 | 100% | 4134.00 | +115.00 | pass | ### summarize definition @@ -74,10 +74,10 @@ Method: `textDocument/definition` | Server | Success | Mean ms | P95 ms | Non-empty % | Definitions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyrefly | yes | 0.19 | 0.19 | 100% | 1.00 | 0.00 | pass | -| Ty | yes | 0.21 | 0.23 | 100% | 1.00 | 0.00 | pass | -| Pyright | yes | 0.39 | 0.44 | 100% | 1.00 | 0.00 | pass | -| pylsp-mypy | yes | 1.03 | 1.06 | 100% | 1.00 | 0.00 | pass | +| Ty | yes | 0.15 | 0.16 | 100% | 1.00 | 0.00 | pass | +| Pyrefly | yes | 0.22 | 0.28 | 100% | 1.00 | 0.00 | pass | +| Pyright | yes | 0.31 | 0.37 | 100% | 1.00 | 0.00 | pass | +| pylsp-mypy | yes | 0.77 | 0.82 | 100% | 1.00 | 0.00 | pass | ### edit array then complete (edit+completion) @@ -85,10 +85,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| pylsp-mypy | no | 4.29 | 4.36 | 0% | 0.00 | -168.00 | fail (10) | -| Ty | yes | 12.59 | 14.17 | 100% | 168.00 | 0.00 | pass | -| Pyrefly | yes | 19.64 | 22.45 | 100% | 149.00 | -19.00 | pass | -| Pyright | yes | 329.85 | 464.42 | 100% | 168.00 | 0.00 | pass | +| pylsp-mypy | no | 3.39 | 3.54 | 0% | 0.00 | -168.00 | fail (10) | +| Ty | yes | 9.33 | 10.83 | 100% | 168.00 | 0.00 | pass | +| Pyrefly | yes | 14.51 | 17.24 | 100% | 149.00 | -19.00 | pass | +| Pyright | yes | 227.49 | 336.93 | 100% | 168.00 | 0.00 | pass | ### edit array then hover (edit+hover) @@ -96,10 +96,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Ty | yes | 5.66 | 5.79 | 100% | 267.00 | -11.00 | pass | -| Pyrefly | yes | 8.36 | 16.17 | 100% | 2546.00 | +2268.00 | pass | -| Pyright | yes | 32.34 | 35.77 | 100% | 278.00 | 0.00 | pass | -| pylsp-mypy | yes | 166.71 | 168.72 | 100% | 5662.00 | +5384.00 | pass | +| Pyrefly | yes | 0.44 | 0.70 | 100% | 2546.00 | +2268.00 | pass | +| Ty | yes | 4.17 | 4.19 | 100% | 267.00 | -11.00 | pass | +| Pyright | yes | 22.64 | 23.85 | 100% | 278.00 | 0.00 | pass | +| pylsp-mypy | yes | 140.00 | 147.20 | 100% | 5662.00 | +5384.00 | pass | ### Result Differences @@ -112,10 +112,10 @@ Method: `textDocument/hover` | Server | Success | Wall clock ms | Avg measured ms | Points | Measured requests | Non-empty % | Failed points | | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | -| Ty | yes | 257.86 | 2.62 | 5 | 25 | 100% | 0 | -| Pyrefly | yes | 285.29 | 4.25 | 5 | 25 | 100% | 0 | -| Pyright | yes | 1461.00 | 13.95 | 5 | 25 | 100% | 0 | -| pylsp-mypy | yes | 7736.99 | 172.00 | 5 | 25 | 100% | 0 | +| Ty | yes | 205.50 | 1.98 | 5 | 25 | 100% | 0 | +| Pyrefly | yes | 206.08 | 2.84 | 5 | 25 | 100% | 0 | +| Pyright | yes | 1137.26 | 11.00 | 5 | 25 | 100% | 0 | +| pylsp-mypy | yes | 6039.29 | 134.50 | 5 | 25 | 100% | 0 | ### queryset completion @@ -123,10 +123,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyright | yes | 4.73 | 7.80 | 100% | 10.00 | 0.00 | pass | -| Ty | yes | 4.81 | 7.16 | 100% | 261.00 | +251.00 | pass | -| Pyrefly | yes | 17.74 | 68.20 | 100% | 15.00 | +5.00 | pass | -| pylsp-mypy | yes | 209.10 | 640.39 | 100% | 2.00 | -8.00 | pass | +| Ty | yes | 3.68 | 5.81 | 100% | 261.00 | +251.00 | pass | +| Pyright | yes | 3.80 | 6.13 | 100% | 10.00 | 0.00 | pass | +| Pyrefly | yes | 11.75 | 45.41 | 100% | 15.00 | +5.00 | pass | +| pylsp-mypy | yes | 162.20 | 508.28 | 100% | 2.00 | -8.00 | pass | ### queryset filter hover @@ -134,10 +134,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Ty | yes | 0.23 | 0.25 | 100% | 46.00 | -11.00 | pass | -| Pyrefly | yes | 0.42 | 0.47 | 100% | 298.00 | +241.00 | pass | -| Pyright | yes | 0.52 | 0.59 | 100% | 57.00 | 0.00 | pass | -| pylsp-mypy | yes | 151.88 | 152.92 | 100% | 57.00 | 0.00 | pass | +| Ty | yes | 0.16 | 0.18 | 100% | 46.00 | -11.00 | pass | +| Pyrefly | yes | 0.20 | 0.21 | 100% | 298.00 | +241.00 | pass | +| Pyright | yes | 0.41 | 0.46 | 100% | 57.00 | 0.00 | pass | +| pylsp-mypy | yes | 128.24 | 128.90 | 100% | 57.00 | 0.00 | pass | ### model definition @@ -145,10 +145,10 @@ Method: `textDocument/definition` | Server | Success | Mean ms | P95 ms | Non-empty % | Definitions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Ty | yes | 0.19 | 0.20 | 100% | 1.00 | 0.00 | pass | -| Pyrefly | yes | 0.37 | 0.41 | 100% | 1.00 | 0.00 | pass | -| Pyright | yes | 0.37 | 0.47 | 100% | 1.00 | 0.00 | pass | -| pylsp-mypy | yes | 1.08 | 1.11 | 100% | 1.00 | 0.00 | pass | +| Ty | yes | 0.13 | 0.13 | 100% | 1.00 | 0.00 | pass | +| Pyrefly | yes | 0.16 | 0.17 | 100% | 1.00 | 0.00 | pass | +| Pyright | yes | 0.30 | 0.34 | 100% | 1.00 | 0.00 | pass | +| pylsp-mypy | yes | 0.75 | 0.78 | 100% | 1.00 | 0.00 | pass | ### edit queryset then complete (edit+completion) @@ -156,10 +156,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyrefly | yes | 1.94 | 2.02 | 100% | 83.00 | -21.00 | pass | -| Ty | yes | 4.73 | 5.04 | 100% | 104.00 | 0.00 | pass | -| Pyright | yes | 25.85 | 28.51 | 100% | 104.00 | 0.00 | pass | -| pylsp-mypy | yes | 255.91 | 289.24 | 100% | 143.00 | +39.00 | pass | +| Pyrefly | yes | 1.49 | 1.57 | 100% | 83.00 | -21.00 | pass | +| Ty | yes | 3.56 | 3.71 | 100% | 104.00 | 0.00 | pass | +| Pyright | yes | 20.27 | 21.67 | 100% | 104.00 | 0.00 | pass | +| pylsp-mypy | yes | 189.08 | 228.11 | 100% | 143.00 | +39.00 | pass | ### edit queryset then hover (edit+hover) @@ -167,10 +167,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyrefly | yes | 0.79 | 0.85 | 100% | 858.00 | +775.00 | pass | -| Ty | yes | 3.16 | 3.20 | 100% | 100.00 | +17.00 | pass | -| Pyright | yes | 38.27 | 43.63 | 100% | 83.00 | 0.00 | pass | -| pylsp-mypy | yes | 242.02 | 243.47 | 100% | 71.00 | -12.00 | pass | +| Pyrefly | yes | 0.61 | 0.63 | 100% | 858.00 | +775.00 | pass | +| Ty | yes | 2.40 | 2.41 | 100% | 100.00 | +17.00 | pass | +| Pyright | yes | 30.20 | 35.72 | 100% | 83.00 | 0.00 | pass | +| pylsp-mypy | yes | 192.22 | 194.27 | 100% | 71.00 | -12.00 | pass | ### Result Differences @@ -183,10 +183,10 @@ Method: `textDocument/hover` | Server | Success | Wall clock ms | Avg measured ms | Points | Measured requests | Non-empty % | Failed points | | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | -| Ty | yes | 832.64 | 7.82 | 5 | 25 | 100% | 0 | -| Pyrefly | yes | 1129.21 | 29.57 | 5 | 25 | 100% | 0 | -| pylsp-mypy | yes | 7632.92 | 136.30 | 5 | 25 | 100% | 0 | -| Pyright | yes | 11274.25 | 185.99 | 5 | 25 | 100% | 0 | +| Ty | yes | 663.86 | 6.24 | 5 | 25 | 100% | 0 | +| Pyrefly | yes | 852.25 | 21.32 | 5 | 25 | 100% | 0 | +| Pyright | yes | 7157.04 | 86.18 | 5 | 25 | 100% | 0 | +| pylsp-mypy | yes | 6286.28 | 106.88 | 5 | 25 | 100% | 0 | ### report dataframe completion @@ -194,10 +194,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Ty | yes | 18.28 | 21.81 | 100% | 1000.00 | +728.80 | pass | -| Pyright | yes | 79.07 | 266.94 | 100% | 271.20 | 0.00 | pass | -| Pyrefly | yes | 87.86 | 347.62 | 100% | 16.00 | -255.20 | pass | -| pylsp-mypy | yes | 92.23 | 216.42 | 100% | 6.00 | -265.20 | pass | +| Ty | yes | 14.11 | 17.17 | 100% | 1000.00 | +728.80 | pass | +| pylsp-mypy | yes | 45.97 | 69.02 | 100% | 6.00 | -265.20 | pass | +| Pyright | yes | 57.90 | 196.85 | 100% | 271.20 | 0.00 | pass | +| Pyrefly | yes | 64.19 | 256.12 | 100% | 16.00 | -255.20 | pass | ### dataframe groupby hover @@ -205,10 +205,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Ty | yes | 0.27 | 0.29 | 100% | 329.00 | -21.00 | pass | -| Pyright | yes | 0.68 | 0.75 | 100% | 350.00 | 0.00 | pass | -| Pyrefly | yes | 3.85 | 5.74 | 100% | 2759.00 | +2409.00 | pass | -| pylsp-mypy | yes | 191.16 | 192.67 | 100% | 301.00 | -49.00 | pass | +| Ty | yes | 0.20 | 0.23 | 100% | 329.00 | -21.00 | pass | +| Pyright | yes | 0.57 | 0.63 | 100% | 350.00 | 0.00 | pass | +| Pyrefly | yes | 4.08 | 7.34 | 100% | 2759.00 | +2409.00 | pass | +| pylsp-mypy | yes | 157.66 | 159.48 | 100% | 301.00 | -49.00 | pass | ### build report definition @@ -216,10 +216,10 @@ Method: `textDocument/definition` | Server | Success | Mean ms | P95 ms | Non-empty % | Definitions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Ty | yes | 0.20 | 0.21 | 100% | 1.00 | 0.00 | pass | -| Pyright | yes | 0.40 | 0.47 | 100% | 1.00 | 0.00 | pass | -| pylsp-mypy | yes | 1.03 | 1.05 | 100% | 1.00 | 0.00 | pass | -| Pyrefly | yes | 2.92 | 2.93 | 100% | 1.00 | 0.00 | pass | +| Ty | yes | 0.14 | 0.15 | 100% | 1.00 | 0.00 | pass | +| Pyright | yes | 0.33 | 0.47 | 100% | 1.00 | 0.00 | pass | +| pylsp-mypy | yes | 0.80 | 0.82 | 100% | 1.00 | 0.00 | pass | +| Pyrefly | yes | 3.53 | 5.36 | 100% | 1.00 | 0.00 | pass | ### edit dataframe then complete (edit+completion) @@ -227,10 +227,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Ty | yes | 15.86 | 17.41 | 100% | 448.00 | +8.00 | pass | -| Pyrefly | yes | 47.65 | 80.12 | 100% | 256.00 | -184.00 | pass | -| pylsp-mypy | yes | 215.62 | 216.90 | 100% | 441.00 | +1.00 | pass | -| Pyright | yes | 838.49 | 1379.15 | 100% | 440.00 | 0.00 | pass | +| Ty | yes | 13.32 | 14.49 | 100% | 448.00 | +8.00 | pass | +| Pyrefly | yes | 27.39 | 34.46 | 100% | 256.00 | -184.00 | pass | +| pylsp-mypy | yes | 182.54 | 187.66 | 100% | 441.00 | +1.00 | pass | +| Pyright | yes | 363.89 | 832.46 | 100% | 440.00 | 0.00 | pass | ### edit dataframe then hover (edit+hover) @@ -238,10 +238,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Ty | yes | 4.47 | 4.48 | 100% | 4441.00 | +149.00 | pass | -| Pyrefly | yes | 5.57 | 13.19 | 100% | 943.00 | -3349.00 | pass | -| Pyright | yes | 11.32 | 13.29 | 100% | 4292.00 | 0.00 | pass | -| pylsp-mypy | yes | 181.45 | 183.44 | 100% | 232.00 | -4060.00 | pass | +| Ty | yes | 3.43 | 3.46 | 100% | 4441.00 | +149.00 | pass | +| Pyrefly | yes | 7.44 | 10.21 | 100% | 943.00 | -3349.00 | pass | +| Pyright | yes | 8.22 | 10.27 | 100% | 4292.00 | 0.00 | pass | +| pylsp-mypy | yes | 147.43 | 150.63 | 100% | 232.00 | -4060.00 | pass | ### Result Differences @@ -254,10 +254,10 @@ Method: `textDocument/hover` | Server | Success | Wall clock ms | Avg measured ms | Points | Measured requests | Non-empty % | Failed points | | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | -| Ty | yes | 374.41 | 2.88 | 5 | 25 | 100% | 0 | -| Pyrefly | yes | 926.41 | 20.92 | 5 | 25 | 100% | 0 | -| Pyright | yes | 3867.76 | 53.42 | 5 | 25 | 100% | 0 | -| pylsp-mypy | no | 7086.84 | 120.64 | 5 | 25 | 60% | 2 | +| Ty | yes | 300.87 | 2.18 | 5 | 25 | 100% | 0 | +| Pyrefly | yes | 765.03 | 17.67 | 5 | 25 | 100% | 0 | +| Pyright | yes | 2925.76 | 39.92 | 5 | 25 | 100% | 0 | +| pylsp-mypy | no | 5987.46 | 92.43 | 5 | 25 | 60% | 2 | ### query completion @@ -265,10 +265,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Ty | yes | 3.33 | 7.41 | 100% | 1.00 | 0.00 | pass | -| Pyright | yes | 14.42 | 26.58 | 100% | 1.00 | 0.00 | pass | -| Pyrefly | yes | 88.10 | 351.56 | 100% | 15.00 | +14.00 | pass | -| pylsp-mypy | yes | 208.83 | 460.44 | 100% | 1.00 | 0.00 | pass | +| Ty | yes | 2.60 | 6.00 | 100% | 1.00 | 0.00 | pass | +| Pyright | yes | 15.68 | 20.85 | 100% | 1.00 | 0.00 | pass | +| Pyrefly | yes | 64.92 | 259.08 | 100% | 15.00 | +14.00 | pass | +| pylsp-mypy | yes | 153.24 | 420.86 | 100% | 1.00 | 0.00 | pass | ### sessionmaker hover @@ -276,10 +276,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Ty | yes | 0.44 | 0.51 | 100% | 10621.00 | +42.00 | pass | -| Pyrefly | yes | 2.49 | 3.32 | 100% | 15239.00 | +4660.00 | pass | -| Pyright | yes | 4.25 | 7.79 | 100% | 10579.00 | 0.00 | pass | -| pylsp-mypy | yes | 318.44 | 321.94 | 100% | 10498.00 | -81.00 | pass | +| Ty | yes | 0.30 | 0.31 | 100% | 10621.00 | +42.00 | pass | +| Pyright | yes | 1.55 | 2.11 | 100% | 10579.00 | 0.00 | pass | +| Pyrefly | yes | 2.92 | 3.44 | 100% | 15239.00 | +4660.00 | pass | +| pylsp-mypy | yes | 248.87 | 253.96 | 100% | 10498.00 | -81.00 | pass | ### mapped class definition @@ -287,10 +287,10 @@ Method: `textDocument/definition` | Server | Success | Mean ms | P95 ms | Non-empty % | Definitions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Ty | yes | 0.21 | 0.23 | 100% | 2.00 | +1.00 | pass | -| Pyright | yes | 0.75 | 1.65 | 100% | 1.00 | 0.00 | pass | -| pylsp-mypy | yes | 1.08 | 1.11 | 100% | 1.00 | 0.00 | pass | -| Pyrefly | yes | 2.20 | 3.67 | 100% | 1.00 | 0.00 | pass | +| Ty | yes | 0.14 | 0.16 | 100% | 2.00 | +1.00 | pass | +| Pyright | yes | 0.34 | 0.40 | 100% | 1.00 | 0.00 | pass | +| pylsp-mypy | yes | 0.83 | 0.94 | 100% | 1.00 | 0.00 | pass | +| Pyrefly | yes | 2.73 | 2.94 | 100% | 1.00 | 0.00 | pass | ### edit query then complete (edit+completion) @@ -298,10 +298,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Ty | yes | 5.36 | 5.56 | 100% | 23.00 | -15.00 | pass | -| Pyrefly | yes | 7.20 | 22.84 | 100% | 17.00 | -21.00 | pass | -| pylsp-mypy | no | 37.64 | 38.27 | 0% | 0.00 | -38.00 | fail (10) | -| Pyright | yes | 145.72 | 176.38 | 100% | 38.00 | 0.00 | pass | +| Ty | yes | 4.10 | 4.51 | 100% | 23.00 | -15.00 | pass | +| Pyrefly | yes | 9.44 | 23.85 | 100% | 17.00 | -21.00 | pass | +| pylsp-mypy | no | 29.67 | 30.00 | 0% | 0.00 | -38.00 | fail (10) | +| Pyright | yes | 107.69 | 134.29 | 100% | 38.00 | 0.00 | pass | ### edit session then hover (edit+hover) @@ -309,10 +309,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyrefly | yes | 4.60 | 12.11 | 100% | 2242.00 | +1349.00 | pass | -| Ty | yes | 5.06 | 5.10 | 100% | 951.00 | +58.00 | pass | -| pylsp-mypy | no | 37.18 | 37.55 | 0% | 0.00 | -893.00 | fail (10) | -| Pyright | yes | 101.94 | 110.98 | 100% | 893.00 | 0.00 | pass | +| Ty | yes | 3.79 | 3.84 | 100% | 951.00 | +58.00 | pass | +| Pyrefly | yes | 8.34 | 23.59 | 100% | 2242.00 | +1349.00 | pass | +| pylsp-mypy | no | 29.53 | 30.42 | 0% | 0.00 | -893.00 | fail (10) | +| Pyright | yes | 74.33 | 81.44 | 100% | 893.00 | 0.00 | pass | ### Result Differences @@ -326,10 +326,10 @@ Method: `textDocument/hover` | Server | Success | Wall clock ms | Avg measured ms | Points | Measured requests | Non-empty % | Failed points | | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | -| Ty | yes | 2761.66 | 4.78 | 5 | 25 | 100% | 0 | -| Pyright | yes | 15845.34 | 108.00 | 5 | 25 | 80% | 0 | -| Pyrefly | yes | 5609.51 | 178.76 | 5 | 25 | 80% | 0 | -| pylsp-mypy | no | 182639.34 | 1538.47 | 5 | 25 | 40% | 2 | +| Ty | yes | 2234.16 | 4.67 | 5 | 25 | 100% | 0 | +| Pyright | yes | 12671.18 | 86.95 | 5 | 25 | 80% | 0 | +| Pyrefly | yes | 3904.83 | 119.91 | 5 | 25 | 80% | 0 | +| pylsp-mypy | no | 137062.20 | 1200.13 | 5 | 25 | 40% | 2 | ### classifier pipeline completion @@ -337,10 +337,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Ty | yes | 11.44 | 12.58 | 100% | 777.00 | +654.00 | pass | -| Pyright | yes | 51.53 | 81.65 | 100% | 123.00 | 0.00 | pass | -| pylsp-mypy | yes | 149.07 | 151.99 | 100% | 2.00 | -121.00 | pass | -| Pyrefly | yes | 877.67 | 3509.79 | 100% | 15.00 | -108.00 | pass | +| Ty | yes | 11.06 | 12.12 | 100% | 777.00 | +654.00 | pass | +| Pyright | yes | 46.54 | 70.06 | 100% | 123.00 | 0.00 | pass | +| pylsp-mypy | yes | 102.33 | 104.37 | 100% | 2.00 | -121.00 | pass | +| Pyrefly | yes | 591.03 | 2363.48 | 100% | 15.00 | -108.00 | pass | ### pipeline hover @@ -348,10 +348,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyrefly | yes | 0.22 | 0.22 | 100% | 48.00 | +14.00 | pass | -| Ty | yes | 0.25 | 0.27 | 100% | 7.00 | -27.00 | pass | -| Pyright | yes | 0.48 | 0.54 | 100% | 34.00 | 0.00 | pass | -| pylsp-mypy | no | 2741.08 | 2774.28 | 0% | 0.00 | -34.00 | fail (10) | +| Pyrefly | yes | 0.14 | 0.17 | 100% | 48.00 | +14.00 | pass | +| Ty | yes | 0.15 | 0.16 | 100% | 7.00 | -27.00 | pass | +| Pyright | yes | 0.38 | 0.46 | 100% | 34.00 | 0.00 | pass | +| pylsp-mypy | no | 2152.58 | 2240.50 | 0% | 0.00 | -34.00 | fail (10) | ### auto tokenizer definition @@ -359,10 +359,10 @@ Method: `textDocument/definition` | Server | Success | Mean ms | P95 ms | Non-empty % | Definitions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyrefly | yes | 0.22 | 0.23 | 100% | 1.00 | 0.00 | pass | -| Ty | yes | 0.28 | 0.28 | 100% | 1.00 | 0.00 | pass | -| Pyright | yes | 0.45 | 0.52 | 100% | 1.00 | 0.00 | pass | -| pylsp-mypy | yes | 2180.97 | 2209.83 | 100% | 1.00 | 0.00 | pass | +| Pyrefly | yes | 0.14 | 0.17 | 100% | 1.00 | 0.00 | pass | +| Ty | yes | 0.18 | 0.19 | 100% | 1.00 | 0.00 | pass | +| Pyright | yes | 0.33 | 0.35 | 100% | 1.00 | 0.00 | pass | +| pylsp-mypy | yes | 1731.78 | 1781.61 | 100% | 1.00 | 0.00 | pass | ### edit prediction then complete (edit+completion) @@ -370,10 +370,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| pylsp-mypy | yes | 2.60 | 2.66 | 0% | 0.00 | 0.00 | pass | -| Ty | yes | 6.12 | 6.49 | 100% | 23.00 | +23.00 | pass | -| Pyright | yes | 6.98 | 9.93 | 0% | 0.00 | 0.00 | pass | -| Pyrefly | yes | 12.67 | 23.94 | 0% | 0.00 | 0.00 | pass | +| pylsp-mypy | yes | 2.11 | 2.35 | 0% | 0.00 | 0.00 | pass | +| Pyrefly | yes | 2.12 | 7.61 | 0% | 0.00 | 0.00 | pass | +| Pyright | yes | 5.20 | 6.65 | 0% | 0.00 | 0.00 | pass | +| Ty | yes | 6.24 | 8.57 | 100% | 23.00 | +23.00 | pass | ### edit tokenizer then hover (edit+hover) @@ -381,10 +381,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyrefly | yes | 3.05 | 3.70 | 100% | 33.00 | +3.00 | pass | -| Ty | yes | 5.81 | 5.92 | 100% | 7.00 | -23.00 | pass | -| Pyright | yes | 480.54 | 505.46 | 100% | 30.00 | 0.00 | pass | -| pylsp-mypy | no | 2618.62 | 2658.80 | 0% | 0.00 | -30.00 | fail (10) | +| Ty | yes | 5.75 | 6.64 | 100% | 7.00 | -23.00 | pass | +| Pyrefly | yes | 6.12 | 13.95 | 100% | 33.00 | +3.00 | pass | +| Pyright | yes | 382.31 | 397.67 | 100% | 30.00 | 0.00 | pass | +| pylsp-mypy | no | 2011.86 | 2032.58 | 0% | 0.00 | -30.00 | fail (10) | ### Result Differences @@ -397,10 +397,10 @@ Method: `textDocument/hover` | Server | Success | Wall clock ms | Avg measured ms | Points | Measured requests | Non-empty % | Failed points | | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | -| Ty | yes | 357.27 | 2.78 | 5 | 25 | 100% | 0 | -| Pyright | yes | 1565.58 | 9.53 | 5 | 25 | 100% | 0 | -| Pyrefly | yes | 864.84 | 12.83 | 5 | 25 | 100% | 0 | -| pylsp-mypy | yes | 4782.06 | 104.69 | 5 | 25 | 100% | 0 | +| Ty | yes | 311.04 | 2.57 | 5 | 25 | 100% | 0 | +| Pyright | yes | 1168.98 | 7.01 | 5 | 25 | 100% | 0 | +| Pyrefly | yes | 681.26 | 10.00 | 5 | 25 | 100% | 0 | +| pylsp-mypy | yes | 3899.92 | 82.22 | 5 | 25 | 100% | 0 | ### request args completion @@ -408,10 +408,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyright | yes | 4.71 | 7.83 | 100% | 14.00 | 0.00 | pass | -| Ty | yes | 5.74 | 8.14 | 100% | 467.00 | +453.00 | pass | -| Pyrefly | yes | 54.96 | 182.88 | 100% | 487.80 | +473.80 | pass | -| pylsp-mypy | yes | 70.75 | 96.72 | 100% | 1.00 | -13.00 | pass | +| Pyright | yes | 3.61 | 6.61 | 100% | 14.00 | 0.00 | pass | +| Ty | yes | 5.27 | 7.37 | 100% | 467.00 | +453.00 | pass | +| Pyrefly | yes | 41.28 | 134.26 | 100% | 487.80 | +473.80 | pass | +| pylsp-mypy | yes | 59.36 | 123.69 | 100% | 1.00 | -13.00 | pass | ### client session hover @@ -419,10 +419,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Ty | yes | 0.21 | 0.23 | 100% | 7.00 | -19.00 | pass | -| Pyright | yes | 0.58 | 0.67 | 100% | 26.00 | 0.00 | pass | -| Pyrefly | yes | 5.19 | 18.16 | 100% | 167.00 | +141.00 | pass | -| pylsp-mypy | yes | 102.40 | 193.33 | 100% | 359.00 | +333.00 | pass | +| Ty | yes | 0.13 | 0.15 | 100% | 7.00 | -19.00 | pass | +| Pyright | yes | 0.39 | 0.45 | 100% | 26.00 | 0.00 | pass | +| Pyrefly | yes | 4.97 | 17.71 | 100% | 167.00 | +141.00 | pass | +| pylsp-mypy | yes | 99.53 | 306.62 | 100% | 359.00 | +333.00 | pass | ### client references @@ -430,10 +430,10 @@ Method: `textDocument/references` | Server | Success | Mean ms | P95 ms | Non-empty % | References found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyrefly | yes | 0.34 | 0.35 | 100% | 2.00 | 0.00 | pass | -| Ty | yes | 0.58 | 0.62 | 100% | 2.00 | 0.00 | pass | -| Pyright | yes | 0.81 | 0.85 | 100% | 2.00 | 0.00 | pass | -| pylsp-mypy | yes | 33.42 | 84.02 | 100% | 2.00 | 0.00 | pass | +| Pyrefly | yes | 0.25 | 0.29 | 100% | 2.00 | 0.00 | pass | +| Ty | yes | 0.47 | 0.56 | 100% | 2.00 | 0.00 | pass | +| Pyright | yes | 1.05 | 2.51 | 100% | 2.00 | 0.00 | pass | +| pylsp-mypy | yes | 3.02 | 4.00 | 100% | 2.00 | 0.00 | pass | ### edit response then complete (edit+completion) @@ -441,10 +441,10 @@ Method: `textDocument/completion` | Server | Success | Mean ms | P95 ms | Non-empty % | Completions found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyrefly | yes | 0.58 | 0.62 | 100% | 32.00 | -173.00 | pass | -| Ty | yes | 4.44 | 4.57 | 100% | 225.00 | +20.00 | pass | -| Pyright | yes | 4.80 | 5.89 | 100% | 205.00 | 0.00 | pass | -| pylsp-mypy | yes | 88.43 | 88.72 | 100% | 57.00 | -148.00 | pass | +| Pyrefly | yes | 0.42 | 0.44 | 100% | 32.00 | -173.00 | pass | +| Ty | yes | 3.82 | 4.43 | 100% | 225.00 | +20.00 | pass | +| Pyright | yes | 3.91 | 5.13 | 100% | 205.00 | 0.00 | pass | +| pylsp-mypy | yes | 62.77 | 64.23 | 100% | 57.00 | -148.00 | pass | ### edit response then hover (edit+hover) @@ -452,10 +452,10 @@ Method: `textDocument/hover` | Server | Success | Mean ms | P95 ms | Non-empty % | Hover length | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Ty | yes | 2.95 | 2.98 | 100% | 1555.00 | +1135.00 | pass | -| Pyrefly | yes | 3.07 | 5.50 | 100% | 9977.00 | +9557.00 | pass | -| Pyright | yes | 36.75 | 39.79 | 100% | 420.00 | 0.00 | pass | -| pylsp-mypy | yes | 228.47 | 230.60 | 100% | 880.00 | +460.00 | pass | +| Pyrefly | yes | 3.08 | 4.69 | 100% | 9977.00 | +9557.00 | pass | +| Ty | yes | 3.14 | 3.61 | 100% | 1555.00 | +1135.00 | pass | +| Pyright | yes | 26.10 | 29.43 | 100% | 420.00 | 0.00 | pass | +| pylsp-mypy | yes | 186.41 | 188.66 | 100% | 880.00 | +460.00 | pass | ### Result Differences @@ -468,7 +468,7 @@ Method: `textDocument/hover` | Server | Success | Wall clock ms | Avg measured ms | Points | Measured requests | Non-empty % | Failed points | | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | -| Pyrefly | no | 223.27 | 0.43 | 8 | 40 | 100% | 2 | +| Pyrefly | no | 177.36 | 0.22 | 8 | 40 | 100% | 2 | ### builtins semantic tokens @@ -476,7 +476,7 @@ Method: semantic token impl using typeServer/getComputedType | Server | Success | Mean ms | P95 ms | Non-empty % | Semantic tokens found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyrefly | yes | 1.85 | 4.39 | 100% | 30.00 | 0.00 | pass | +| Pyrefly | yes | 0.74 | 0.78 | 100% | 30.00 | 0.00 | pass | ### builtin int computed type @@ -484,7 +484,7 @@ Method: `typeServer/getComputedType` | Server | Success | Mean ms | P95 ms | Non-empty % | Results found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyrefly | yes | 0.21 | 0.22 | 100% | 7.00 | 0.00 | pass | +| Pyrefly | yes | 0.13 | 0.14 | 100% | 7.00 | 0.00 | pass | ### list declared type @@ -492,7 +492,7 @@ Method: `typeServer/getDeclaredType` | Server | Success | Mean ms | P95 ms | Non-empty % | Results found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyrefly | yes | 0.24 | 0.25 | 100% | 7.00 | 0.00 | pass | +| Pyrefly | yes | 0.16 | 0.17 | 100% | 7.00 | 0.00 | pass | ### generic specialization computed type @@ -500,7 +500,7 @@ Method: `typeServer/getComputedType` | Server | Success | Mean ms | P95 ms | Non-empty % | Results found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyrefly | yes | 0.25 | 0.26 | 100% | 7.00 | 0.00 | pass | +| Pyrefly | yes | 0.16 | 0.18 | 100% | 7.00 | 0.00 | pass | ### stdlib path computed type @@ -508,7 +508,7 @@ Method: `typeServer/getComputedType` | Server | Success | Mean ms | P95 ms | Non-empty % | Results found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyrefly | yes | 0.21 | 0.21 | 100% | 7.00 | 0.00 | pass | +| Pyrefly | yes | 0.16 | 0.19 | 100% | 7.00 | 0.00 | pass | ### function argument expected type @@ -516,13 +516,13 @@ Method: `typeServer/getExpectedType` | Server | Success | Mean ms | P95 ms | Non-empty % | Results found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyrefly | yes | 0.25 | 0.27 | 100% | 7.00 | 0.00 | pass | +| Pyrefly | yes | 0.15 | 0.16 | 100% | 7.00 | 0.00 | pass | ## Benchmark: tsp_semantic | Server | Success | Wall clock ms | Avg measured ms | Points | Measured requests | Non-empty % | Failed points | | --- | --- | ---: | ---: | ---: | ---: | ---: | ---: | -| Pyrefly | yes | 6343.76 | 66.56 | 3 | 15 | 100% | 0 | +| Pyrefly | yes | 4325.51 | 21.44 | 3 | 15 | 100% | 0 | ### django semantic tokens @@ -530,7 +530,7 @@ Method: semantic token impl using typeServer/getComputedType | Server | Success | Mean ms | P95 ms | Non-empty % | Semantic tokens found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyrefly | yes | 17.65 | 36.64 | 100% | 126.00 | 0.00 | pass | +| Pyrefly | yes | 9.57 | 18.50 | 100% | 126.00 | 0.00 | pass | ### transformers semantic tokens @@ -538,7 +538,7 @@ Method: semantic token impl using typeServer/getComputedType | Server | Success | Mean ms | P95 ms | Non-empty % | Semantic tokens found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyrefly | yes | 122.07 | 150.74 | 100% | 74.00 | 0.00 | pass | +| Pyrefly | yes | 45.62 | 47.40 | 100% | 74.00 | 0.00 | pass | ### stdlib semantic tokens @@ -546,4 +546,4 @@ Method: semantic token impl using typeServer/getComputedType | Server | Success | Mean ms | P95 ms | Non-empty % | Semantic tokens found | Delta vs Pyright | Validation | | --- | --- | ---: | ---: | ---: | ---: | ---: | --- | -| Pyrefly | yes | 59.96 | 73.59 | 100% | 75.00 | 0.00 | pass | +| Pyrefly | yes | 9.12 | 21.59 | 100% | 75.00 | 0.00 | pass | diff --git a/latest-results/ty-20260928T060730Z-responses.jsonl b/latest-results/ty-20260929T060553Z-responses.jsonl similarity index 99% rename from latest-results/ty-20260928T060730Z-responses.jsonl rename to latest-results/ty-20260929T060553Z-responses.jsonl index 5615ccf..9a92579 100644 --- a/latest-results/ty-20260928T060730Z-responses.jsonl +++ b/latest-results/ty-20260929T060553Z-responses.jsonl @@ -83,11 +83,11 @@ {"suite": "sqlalchemy", "label": "sessionmaker hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 12, "character": 27, "iteration": 3, "result": {"contents": {"kind": "plaintext", "value": "def mapped_column[_T](\n __name_pos: str | type[TypeEngine[Any]] | TypeEngine[Any] | SchemaEventTarget | None = None,\n __type_pos: type[TypeEngine[Any]] | TypeEngine[Any] | SchemaEventTarget | None = None,\n /,\n *args: SchemaEventTarget,\n init: _NoArg | bool = ...,\n repr: _NoArg | bool = ...,\n default: Any | None = ...,\n default_factory: _NoArg | (() -> _T) = ...,\n compare: _NoArg | bool = ...,\n kw_only: _NoArg | bool = ...,\n hash: _NoArg | bool | None = ...,\n nullable: bool | Literal[SchemaConst.NULL_UNSPECIFIED] | None = SchemaConst.NULL_UNSPECIFIED,\n primary_key: bool | None = False,\n deferred: _NoArg | bool = ...,\n deferred_group: str | None = None,\n deferred_raiseload: bool | None = None,\n use_existing_column: bool = False,\n name: str | None = None,\n type_: type[TypeEngine[Any]] | TypeEngine[Any] | None = None,\n autoincrement: bool | Literal[\"auto\", \"ignore_fk\"] = \"auto\",\n doc: str | None = None,\n key: str | None = None,\n index: bool | None = None,\n unique: bool | None = None,\n info: dict[Any, Any] | None = None,\n onupdate: Any | None = None,\n insert_default: Any | None = ...,\n server_default: FetchedValue | str | TextClause | ColumnElement[Any] | None = None,\n server_onupdate: FetchedValue | str | TextClause | ColumnElement[Any] | None = None,\n active_history: bool = False,\n quote: bool | None = None,\n system: bool = False,\n comment: str | None = None,\n sort_order: _NoArg | int = ...,\n dataclass_metadata: _NoArg | Mapping[Any, Any] | None = ...,\n **kw: Any\n) -> MappedColumn[Any]\n---------------------------------------------\ndeclare a new ORM-mapped :class:`_schema.Column` construct\nfor use within :ref:`Declarative Table `\nconfiguration.\n\nThe :func:`_orm.mapped_column` function provides an ORM-aware and\nPython-typing-compatible construct which is used with\n:ref:`declarative ` mappings to indicate an\nattribute that's mapped to a Core :class:`_schema.Column` object. It\nprovides the equivalent feature as mapping an attribute to a\n:class:`_schema.Column` object directly when using Declarative,\nspecifically when using :ref:`Declarative Table `\nconfiguration.\n\n.. versionadded:: 2.0\n\n:func:`_orm.mapped_column` is normally used with explicit typing along with\nthe :class:`_orm.Mapped` annotation type, where it can derive the SQL\ntype and nullability for the column based on what's present within the\n:class:`_orm.Mapped` annotation. It also may be used without annotations\nas a drop-in replacement for how :class:`_schema.Column` is used in\nDeclarative mappings in SQLAlchemy 1.x style.\n\nFor usage examples of :func:`_orm.mapped_column`, see the documentation\nat :ref:`orm_declarative_table`.\n\n.. seealso::\n\n :ref:`orm_declarative_table` - complete documentation\n\n :ref:`whatsnew_20_orm_declarative_typing` - migration notes for\n Declarative mappings using 1.x style mappings\n\n:param __name: String name to give to the :class:`_schema.Column`. This\n is an optional, positional only argument that if present must be the\n first positional argument passed. If omitted, the attribute name to\n which the :func:`_orm.mapped_column` is mapped will be used as the SQL\n column name.\n:param __type: :class:`_types.TypeEngine` type or instance which will\n indicate the datatype to be associated with the :class:`_schema.Column`.\n This is an optional, positional-only argument that if present must\n immediately follow the ``__name`` parameter if present also, or otherwise\n be the first positional parameter. If omitted, the ultimate type for\n the column may be derived either from the annotated type, or if a\n :class:`_schema.ForeignKey` is present, from the datatype of the\n referenced column.\n:param \\*args: Additional positional arguments include constructs such\n as :class:`_schema.ForeignKey`, :class:`_schema.CheckConstraint`,\n and :class:`_schema.Identity`, which are passed through to the constructed\n :class:`_schema.Column`.\n:param nullable: Optional bool, whether the column should be \"NULL\" or\n \"NOT NULL\". If omitted, the nullability is derived from the type\n annotation based on whether or not ``typing.Optional`` (or its equivalent)\n is present. ``nullable`` defaults to ``True`` otherwise for non-primary\n key columns, and ``False`` for primary key columns.\n:param primary_key: optional bool, indicates the :class:`_schema.Column`\n would be part of the table's primary key or not.\n:param deferred: Optional bool - this keyword argument is consumed by the\n ORM declarative process, and is not part of the :class:`_schema.Column`\n itself; instead, it indicates that this column should be \"deferred\" for\n loading as though mapped by :func:`_orm.deferred`.\n\n .. seealso::\n\n :ref:`orm_queryguide_deferred_declarative`\n\n:param deferred_group: Implies :paramref:`_orm.mapped_column.deferred`\n to ``True``, and set the :paramref:`_orm.deferred.group` parameter.\n\n .. seealso::\n\n :ref:`orm_queryguide_deferred_group`\n\n:param deferred_raiseload: Implies :paramref:`_orm.mapped_column.deferred`\n to ``True``, and set the :paramref:`_orm.deferred.raiseload` parameter.\n\n .. seealso::\n\n :ref:`orm_queryguide_deferred_raiseload`\n\n:param use_existing_column: if True, will attempt to locate the given\n column name on an inherited superclass (typically single inheriting\n superclass), and if present, will not produce a new column, mapping\n to the superclass column as though it were omitted from this class.\n This is used for mixins that add new columns to an inherited superclass.\n\n .. seealso::\n\n :ref:`orm_inheritance_column_conflicts`\n\n .. versionadded:: 2.0.0b4\n\n:param default: Passed directly to the\n :paramref:`_schema.Column.default` parameter if the\n :paramref:`_orm.mapped_column.insert_default` parameter is not present.\n Additionally, when used with :ref:`orm_declarative_native_dataclasses`,\n indicates a default Python value that should be applied to the keyword\n constructor within the generated ``__init__()`` method.\n\n Note that in the case of dataclass generation when\n :paramref:`_orm.mapped_column.insert_default` is not present, this means\n the :paramref:`_orm.mapped_column.default` value is used in **two**\n places, both the ``__init__()`` method as well as the\n :paramref:`_schema.Column.default` parameter. While this behavior may\n change in a future release, for the moment this tends to \"work out\"; a\n default of ``None`` will mean that the :class:`_schema.Column` gets no\n default generator, whereas a default that refers to a non-``None`` Python\n or SQL expression value will be assigned up front on the object when\n ``__init__()`` is called, which is the same value that the Core\n :class:`_sql.Insert` construct would use in any case, leading to the same\n end result.\n\n .. note:: When using Core level column defaults that are callables to\n be interpreted by the underlying :class:`_schema.Column` in conjunction\n with :ref:`ORM-mapped dataclasses\n `, especially those that are\n :ref:`context-aware default functions `,\n **the** :paramref:`_orm.mapped_column.insert_default` **parameter must\n be used instead**. This is necessary to disambiguate the callable from\n being interpreted as a dataclass level default.\n\n .. seealso::\n\n :ref:`defaults_default_factory_insert_default`\n\n :paramref:`_orm.mapped_column.insert_default`\n\n :paramref:`_orm.mapped_column.default_factory`\n\n:param insert_default: Passed directly to the\n :paramref:`_schema.Column.default` parameter; will supersede the value\n of :paramref:`_orm.mapped_column.default` when present, however\n :paramref:`_orm.mapped_column.default` will always apply to the\n constructor default for a dataclasses mapping.\n\n .. seealso::\n\n :ref:`defaults_default_factory_insert_default`\n\n :paramref:`_orm.mapped_column.default`\n\n :paramref:`_orm.mapped_column.default_factory`\n\n:param sort_order: An integer that indicates how this mapped column\n should be sorted compared to the others when the ORM is creating a\n :class:`_schema.Table`. Among mapped columns that have the same\n value the default ordering is used, placing first the mapped columns\n defined in the main class, then the ones in the super classes.\n Defaults to 0. The sort is ascending.\n\n .. versionadded:: 2.0.4\n\n:param active_history=False:\n\n When ``True``, indicates that the \"previous\" value for a\n scalar attribute should be loaded when replaced, if not\n already loaded. Normally, history tracking logic for\n simple non-primary-key scalar values only needs to be\n aware of the \"new\" value in order to perform a flush. This\n flag is available for applications that make use of\n :func:`.attributes.get_history` or :meth:`.Session.is_modified`\n which also need to know the \"previous\" value of the attribute.\n\n .. versionadded:: 2.0.10\n\n\n:param init: Specific to :ref:`orm_declarative_native_dataclasses`,\n specifies if the mapped attribute should be part of the ``__init__()``\n method as generated by the dataclass process.\n:param repr: Specific to :ref:`orm_declarative_native_dataclasses`,\n specifies if the mapped attribute should be part of the ``__repr__()``\n method as generated by the dataclass process.\n:param default_factory: Specific to\n :ref:`orm_declarative_native_dataclasses`,\n specifies a default-value generation function that will take place\n as part of the ``__init__()``\n method as generated by the dataclass process.\n\n .. seealso::\n\n :ref:`defaults_default_factory_insert_default`\n\n :paramref:`_orm.mapped_column.default`\n\n :paramref:`_orm.mapped_column.insert_default`\n\n:param compare: Specific to\n :ref:`orm_declarative_native_dataclasses`, indicates if this field\n should be included in comparison operations when generating the\n ``__eq__()`` and ``__ne__()`` methods for the mapped class.\n\n .. versionadded:: 2.0.0b4\n\n:param kw_only: Specific to\n :ref:`orm_declarative_native_dataclasses`, indicates if this field\n should be marked as keyword-only when generating the ``__init__()``.\n\n:param hash: Specific to\n :ref:`orm_declarative_native_dataclasses`, controls if this field\n is included when generating the ``__hash__()`` method for the mapped\n class.\n\n .. versionadded:: 2.0.36\n\n:param dataclass_metadata: Specific to\n :ref:`orm_declarative_native_dataclasses`, supplies metadata\n to be attached to the generated dataclass field.\n\n .. versionadded:: 2.0.42\n\n:param \\**kw: All remaining keyword arguments are passed through to the\n constructor for the :class:`_schema.Column`.\n"}, "range": {"end": {"character": 37, "line": 12}, "start": {"character": 24, "line": 12}}}} {"suite": "sqlalchemy", "label": "sessionmaker hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 12, "character": 27, "iteration": 4, "result": {"contents": {"kind": "plaintext", "value": "def mapped_column[_T](\n __name_pos: str | type[TypeEngine[Any]] | TypeEngine[Any] | SchemaEventTarget | None = None,\n __type_pos: type[TypeEngine[Any]] | TypeEngine[Any] | SchemaEventTarget | None = None,\n /,\n *args: SchemaEventTarget,\n init: _NoArg | bool = ...,\n repr: _NoArg | bool = ...,\n default: Any | None = ...,\n default_factory: _NoArg | (() -> _T) = ...,\n compare: _NoArg | bool = ...,\n kw_only: _NoArg | bool = ...,\n hash: _NoArg | bool | None = ...,\n nullable: bool | Literal[SchemaConst.NULL_UNSPECIFIED] | None = SchemaConst.NULL_UNSPECIFIED,\n primary_key: bool | None = False,\n deferred: _NoArg | bool = ...,\n deferred_group: str | None = None,\n deferred_raiseload: bool | None = None,\n use_existing_column: bool = False,\n name: str | None = None,\n type_: type[TypeEngine[Any]] | TypeEngine[Any] | None = None,\n autoincrement: bool | Literal[\"auto\", \"ignore_fk\"] = \"auto\",\n doc: str | None = None,\n key: str | None = None,\n index: bool | None = None,\n unique: bool | None = None,\n info: dict[Any, Any] | None = None,\n onupdate: Any | None = None,\n insert_default: Any | None = ...,\n server_default: FetchedValue | str | TextClause | ColumnElement[Any] | None = None,\n server_onupdate: FetchedValue | str | TextClause | ColumnElement[Any] | None = None,\n active_history: bool = False,\n quote: bool | None = None,\n system: bool = False,\n comment: str | None = None,\n sort_order: _NoArg | int = ...,\n dataclass_metadata: _NoArg | Mapping[Any, Any] | None = ...,\n **kw: Any\n) -> MappedColumn[Any]\n---------------------------------------------\ndeclare a new ORM-mapped :class:`_schema.Column` construct\nfor use within :ref:`Declarative Table `\nconfiguration.\n\nThe :func:`_orm.mapped_column` function provides an ORM-aware and\nPython-typing-compatible construct which is used with\n:ref:`declarative ` mappings to indicate an\nattribute that's mapped to a Core :class:`_schema.Column` object. It\nprovides the equivalent feature as mapping an attribute to a\n:class:`_schema.Column` object directly when using Declarative,\nspecifically when using :ref:`Declarative Table `\nconfiguration.\n\n.. versionadded:: 2.0\n\n:func:`_orm.mapped_column` is normally used with explicit typing along with\nthe :class:`_orm.Mapped` annotation type, where it can derive the SQL\ntype and nullability for the column based on what's present within the\n:class:`_orm.Mapped` annotation. It also may be used without annotations\nas a drop-in replacement for how :class:`_schema.Column` is used in\nDeclarative mappings in SQLAlchemy 1.x style.\n\nFor usage examples of :func:`_orm.mapped_column`, see the documentation\nat :ref:`orm_declarative_table`.\n\n.. seealso::\n\n :ref:`orm_declarative_table` - complete documentation\n\n :ref:`whatsnew_20_orm_declarative_typing` - migration notes for\n Declarative mappings using 1.x style mappings\n\n:param __name: String name to give to the :class:`_schema.Column`. This\n is an optional, positional only argument that if present must be the\n first positional argument passed. If omitted, the attribute name to\n which the :func:`_orm.mapped_column` is mapped will be used as the SQL\n column name.\n:param __type: :class:`_types.TypeEngine` type or instance which will\n indicate the datatype to be associated with the :class:`_schema.Column`.\n This is an optional, positional-only argument that if present must\n immediately follow the ``__name`` parameter if present also, or otherwise\n be the first positional parameter. If omitted, the ultimate type for\n the column may be derived either from the annotated type, or if a\n :class:`_schema.ForeignKey` is present, from the datatype of the\n referenced column.\n:param \\*args: Additional positional arguments include constructs such\n as :class:`_schema.ForeignKey`, :class:`_schema.CheckConstraint`,\n and :class:`_schema.Identity`, which are passed through to the constructed\n :class:`_schema.Column`.\n:param nullable: Optional bool, whether the column should be \"NULL\" or\n \"NOT NULL\". If omitted, the nullability is derived from the type\n annotation based on whether or not ``typing.Optional`` (or its equivalent)\n is present. ``nullable`` defaults to ``True`` otherwise for non-primary\n key columns, and ``False`` for primary key columns.\n:param primary_key: optional bool, indicates the :class:`_schema.Column`\n would be part of the table's primary key or not.\n:param deferred: Optional bool - this keyword argument is consumed by the\n ORM declarative process, and is not part of the :class:`_schema.Column`\n itself; instead, it indicates that this column should be \"deferred\" for\n loading as though mapped by :func:`_orm.deferred`.\n\n .. seealso::\n\n :ref:`orm_queryguide_deferred_declarative`\n\n:param deferred_group: Implies :paramref:`_orm.mapped_column.deferred`\n to ``True``, and set the :paramref:`_orm.deferred.group` parameter.\n\n .. seealso::\n\n :ref:`orm_queryguide_deferred_group`\n\n:param deferred_raiseload: Implies :paramref:`_orm.mapped_column.deferred`\n to ``True``, and set the :paramref:`_orm.deferred.raiseload` parameter.\n\n .. seealso::\n\n :ref:`orm_queryguide_deferred_raiseload`\n\n:param use_existing_column: if True, will attempt to locate the given\n column name on an inherited superclass (typically single inheriting\n superclass), and if present, will not produce a new column, mapping\n to the superclass column as though it were omitted from this class.\n This is used for mixins that add new columns to an inherited superclass.\n\n .. seealso::\n\n :ref:`orm_inheritance_column_conflicts`\n\n .. versionadded:: 2.0.0b4\n\n:param default: Passed directly to the\n :paramref:`_schema.Column.default` parameter if the\n :paramref:`_orm.mapped_column.insert_default` parameter is not present.\n Additionally, when used with :ref:`orm_declarative_native_dataclasses`,\n indicates a default Python value that should be applied to the keyword\n constructor within the generated ``__init__()`` method.\n\n Note that in the case of dataclass generation when\n :paramref:`_orm.mapped_column.insert_default` is not present, this means\n the :paramref:`_orm.mapped_column.default` value is used in **two**\n places, both the ``__init__()`` method as well as the\n :paramref:`_schema.Column.default` parameter. While this behavior may\n change in a future release, for the moment this tends to \"work out\"; a\n default of ``None`` will mean that the :class:`_schema.Column` gets no\n default generator, whereas a default that refers to a non-``None`` Python\n or SQL expression value will be assigned up front on the object when\n ``__init__()`` is called, which is the same value that the Core\n :class:`_sql.Insert` construct would use in any case, leading to the same\n end result.\n\n .. note:: When using Core level column defaults that are callables to\n be interpreted by the underlying :class:`_schema.Column` in conjunction\n with :ref:`ORM-mapped dataclasses\n `, especially those that are\n :ref:`context-aware default functions `,\n **the** :paramref:`_orm.mapped_column.insert_default` **parameter must\n be used instead**. This is necessary to disambiguate the callable from\n being interpreted as a dataclass level default.\n\n .. seealso::\n\n :ref:`defaults_default_factory_insert_default`\n\n :paramref:`_orm.mapped_column.insert_default`\n\n :paramref:`_orm.mapped_column.default_factory`\n\n:param insert_default: Passed directly to the\n :paramref:`_schema.Column.default` parameter; will supersede the value\n of :paramref:`_orm.mapped_column.default` when present, however\n :paramref:`_orm.mapped_column.default` will always apply to the\n constructor default for a dataclasses mapping.\n\n .. seealso::\n\n :ref:`defaults_default_factory_insert_default`\n\n :paramref:`_orm.mapped_column.default`\n\n :paramref:`_orm.mapped_column.default_factory`\n\n:param sort_order: An integer that indicates how this mapped column\n should be sorted compared to the others when the ORM is creating a\n :class:`_schema.Table`. Among mapped columns that have the same\n value the default ordering is used, placing first the mapped columns\n defined in the main class, then the ones in the super classes.\n Defaults to 0. The sort is ascending.\n\n .. versionadded:: 2.0.4\n\n:param active_history=False:\n\n When ``True``, indicates that the \"previous\" value for a\n scalar attribute should be loaded when replaced, if not\n already loaded. Normally, history tracking logic for\n simple non-primary-key scalar values only needs to be\n aware of the \"new\" value in order to perform a flush. This\n flag is available for applications that make use of\n :func:`.attributes.get_history` or :meth:`.Session.is_modified`\n which also need to know the \"previous\" value of the attribute.\n\n .. versionadded:: 2.0.10\n\n\n:param init: Specific to :ref:`orm_declarative_native_dataclasses`,\n specifies if the mapped attribute should be part of the ``__init__()``\n method as generated by the dataclass process.\n:param repr: Specific to :ref:`orm_declarative_native_dataclasses`,\n specifies if the mapped attribute should be part of the ``__repr__()``\n method as generated by the dataclass process.\n:param default_factory: Specific to\n :ref:`orm_declarative_native_dataclasses`,\n specifies a default-value generation function that will take place\n as part of the ``__init__()``\n method as generated by the dataclass process.\n\n .. seealso::\n\n :ref:`defaults_default_factory_insert_default`\n\n :paramref:`_orm.mapped_column.default`\n\n :paramref:`_orm.mapped_column.insert_default`\n\n:param compare: Specific to\n :ref:`orm_declarative_native_dataclasses`, indicates if this field\n should be included in comparison operations when generating the\n ``__eq__()`` and ``__ne__()`` methods for the mapped class.\n\n .. versionadded:: 2.0.0b4\n\n:param kw_only: Specific to\n :ref:`orm_declarative_native_dataclasses`, indicates if this field\n should be marked as keyword-only when generating the ``__init__()``.\n\n:param hash: Specific to\n :ref:`orm_declarative_native_dataclasses`, controls if this field\n is included when generating the ``__hash__()`` method for the mapped\n class.\n\n .. versionadded:: 2.0.36\n\n:param dataclass_metadata: Specific to\n :ref:`orm_declarative_native_dataclasses`, supplies metadata\n to be attached to the generated dataclass field.\n\n .. versionadded:: 2.0.42\n\n:param \\**kw: All remaining keyword arguments are passed through to the\n constructor for the :class:`_schema.Column`.\n"}, "range": {"end": {"character": 37, "line": 12}, "start": {"character": 24, "line": 12}}}} {"suite": "sqlalchemy", "label": "sessionmaker hover", "method": "textDocument/hover", "file_path": "/home/runner/work/python-lsp-compare/python-lsp-compare/benchmarks/sqlalchemy/src/models.py", "line": 12, "character": 27, "iteration": 5, "result": {"contents": {"kind": "plaintext", "value": "def mapped_column[_T](\n __name_pos: str | type[TypeEngine[Any]] | TypeEngine[Any] | SchemaEventTarget | None = None,\n __type_pos: type[TypeEngine[Any]] | TypeEngine[Any] | SchemaEventTarget | None = None,\n /,\n *args: SchemaEventTarget,\n init: _NoArg | bool = ...,\n repr: _NoArg | bool = ...,\n default: Any | None = ...,\n default_factory: _NoArg | (() -> _T) = ...,\n compare: _NoArg | bool = ...,\n kw_only: _NoArg | bool = ...,\n hash: _NoArg | bool | None = ...,\n nullable: bool | Literal[SchemaConst.NULL_UNSPECIFIED] | None = SchemaConst.NULL_UNSPECIFIED,\n primary_key: bool | None = False,\n deferred: _NoArg | bool = ...,\n deferred_group: str | None = None,\n deferred_raiseload: bool | None = None,\n use_existing_column: bool = False,\n name: str | None = None,\n type_: type[TypeEngine[Any]] | TypeEngine[Any] | None = None,\n autoincrement: bool | Literal[\"auto\", \"ignore_fk\"] = \"auto\",\n doc: str | None = None,\n key: str | None = None,\n index: bool | None = None,\n unique: bool | None = None,\n info: dict[Any, Any] | None = None,\n onupdate: Any | None = None,\n insert_default: Any | None = ...,\n server_default: FetchedValue | str | TextClause | ColumnElement[Any] | None = None,\n server_onupdate: FetchedValue | str | TextClause | ColumnElement[Any] | None = None,\n active_history: bool = False,\n quote: bool | None = None,\n system: bool = False,\n comment: str | None = None,\n sort_order: _NoArg | int = ...,\n dataclass_metadata: _NoArg | Mapping[Any, Any] | None = ...,\n **kw: Any\n) -> MappedColumn[Any]\n---------------------------------------------\ndeclare a new ORM-mapped :class:`_schema.Column` construct\nfor use within :ref:`Declarative Table `\nconfiguration.\n\nThe :func:`_orm.mapped_column` function provides an ORM-aware and\nPython-typing-compatible construct which is used with\n:ref:`declarative ` mappings to indicate an\nattribute that's mapped to a Core :class:`_schema.Column` object. It\nprovides the equivalent feature as mapping an attribute to a\n:class:`_schema.Column` object directly when using Declarative,\nspecifically when using :ref:`Declarative Table `\nconfiguration.\n\n.. versionadded:: 2.0\n\n:func:`_orm.mapped_column` is normally used with explicit typing along with\nthe :class:`_orm.Mapped` annotation type, where it can derive the SQL\ntype and nullability for the column based on what's present within the\n:class:`_orm.Mapped` annotation. It also may be used without annotations\nas a drop-in replacement for how :class:`_schema.Column` is used in\nDeclarative mappings in SQLAlchemy 1.x style.\n\nFor usage examples of :func:`_orm.mapped_column`, see the documentation\nat :ref:`orm_declarative_table`.\n\n.. seealso::\n\n :ref:`orm_declarative_table` - complete documentation\n\n :ref:`whatsnew_20_orm_declarative_typing` - migration notes for\n Declarative mappings using 1.x style mappings\n\n:param __name: String name to give to the :class:`_schema.Column`. This\n is an optional, positional only argument that if present must be the\n first positional argument passed. If omitted, the attribute name to\n which the :func:`_orm.mapped_column` is mapped will be used as the SQL\n column name.\n:param __type: :class:`_types.TypeEngine` type or instance which will\n indicate the datatype to be associated with the :class:`_schema.Column`.\n This is an optional, positional-only argument that if present must\n immediately follow the ``__name`` parameter if present also, or otherwise\n be the first positional parameter. If omitted, the ultimate type for\n the column may be derived either from the annotated type, or if a\n :class:`_schema.ForeignKey` is present, from the datatype of the\n referenced column.\n:param \\*args: Additional positional arguments include constructs such\n as :class:`_schema.ForeignKey`, :class:`_schema.CheckConstraint`,\n and :class:`_schema.Identity`, which are passed through to the constructed\n :class:`_schema.Column`.\n:param nullable: Optional bool, whether the column should be \"NULL\" or\n \"NOT NULL\". If omitted, the nullability is derived from the type\n annotation based on whether or not ``typing.Optional`` (or its equivalent)\n is present. ``nullable`` defaults to ``True`` otherwise for non-primary\n key columns, and ``False`` for primary key columns.\n:param primary_key: optional bool, indicates the :class:`_schema.Column`\n would be part of the table's primary key or not.\n:param deferred: Optional bool - this keyword argument is consumed by the\n ORM declarative process, and is not part of the :class:`_schema.Column`\n itself; instead, it indicates that this column should be \"deferred\" for\n loading as though mapped by :func:`_orm.deferred`.\n\n .. seealso::\n\n :ref:`orm_queryguide_deferred_declarative`\n\n:param deferred_group: Implies :paramref:`_orm.mapped_column.deferred`\n to ``True``, and set the :paramref:`_orm.deferred.group` parameter.\n\n .. seealso::\n\n :ref:`orm_queryguide_deferred_group`\n\n:param deferred_raiseload: Implies :paramref:`_orm.mapped_column.deferred`\n to ``True``, and set the :paramref:`_orm.deferred.raiseload` parameter.\n\n .. seealso::\n\n :ref:`orm_queryguide_deferred_raiseload`\n\n:param use_existing_column: if True, will attempt to locate the given\n column name on an inherited superclass (typically single inheriting\n superclass), and if present, will not produce a new column, mapping\n to the superclass column as though it were omitted from this class.\n This is used for mixins that add new columns to an inherited superclass.\n\n .. seealso::\n\n :ref:`orm_inheritance_column_conflicts`\n\n .. versionadded:: 2.0.0b4\n\n:param default: Passed directly to the\n :paramref:`_schema.Column.default` parameter if the\n :paramref:`_orm.mapped_column.insert_default` parameter is not present.\n Additionally, when used with :ref:`orm_declarative_native_dataclasses`,\n indicates a default Python value that should be applied to the keyword\n constructor within the generated ``__init__()`` method.\n\n Note that in the case of dataclass generation when\n :paramref:`_orm.mapped_column.insert_default` is not present, this means\n the :paramref:`_orm.mapped_column.default` value is used in **two**\n places, both the ``__init__()`` method as well as the\n :paramref:`_schema.Column.default` parameter. While this behavior may\n change in a future release, for the moment this tends to \"work out\"; a\n default of ``None`` will mean that the :class:`_schema.Column` gets no\n default generator, whereas a default that refers to a non-``None`` Python\n or SQL expression value will be assigned up front on the object when\n ``__init__()`` is called, which is the same value that the Core\n :class:`_sql.Insert` construct would use in any case, leading to the same\n end result.\n\n .. note:: When using Core level column defaults that are callables to\n be interpreted by the underlying :class:`_schema.Column` in conjunction\n with :ref:`ORM-mapped dataclasses\n `, especially those that are\n :ref:`context-aware default functions `,\n **the** :paramref:`_orm.mapped_column.insert_default` **parameter must\n be used instead**. This is necessary to disambiguate the callable from\n being interpreted as a dataclass level default.\n\n .. seealso::\n\n :ref:`defaults_default_factory_insert_default`\n\n :paramref:`_orm.mapped_column.insert_default`\n\n :paramref:`_orm.mapped_column.default_factory`\n\n:param insert_default: Passed directly to the\n :paramref:`_schema.Column.default` parameter; will supersede the value\n of :paramref:`_orm.mapped_column.default` when present, however\n :paramref:`_orm.mapped_column.default` will always apply to the\n constructor default for a dataclasses mapping.\n\n .. seealso::\n\n :ref:`defaults_default_factory_insert_default`\n\n :paramref:`_orm.mapped_column.default`\n\n :paramref:`_orm.mapped_column.default_factory`\n\n:param sort_order: An integer that indicates how this mapped column\n should be sorted compared to the others when the ORM is creating a\n :class:`_schema.Table`. Among mapped columns that have the same\n value the default ordering is used, placing first the mapped columns\n defined in the main class, then the ones in the super classes.\n Defaults to 0. The sort is ascending.\n\n .. versionadded:: 2.0.4\n\n:param active_history=False:\n\n When ``True``, indicates that the \"previous\" value for a\n scalar attribute should be loaded when replaced, if not\n already loaded. Normally, history tracking logic for\n simple non-primary-key scalar values only needs to be\n aware of the \"new\" value in order to perform a flush. This\n flag is available for applications that make use of\n :func:`.attributes.get_history` or :meth:`.Session.is_modified`\n which also need to know the \"previous\" value of the attribute.\n\n .. versionadded:: 2.0.10\n\n\n:param init: Specific to :ref:`orm_declarative_native_dataclasses`,\n specifies if the mapped attribute should be part of the ``__init__()``\n method as generated by the dataclass process.\n:param repr: Specific to :ref:`orm_declarative_native_dataclasses`,\n specifies if the mapped attribute should be part of the ``__repr__()``\n method as generated by the dataclass process.\n:param default_factory: Specific to\n :ref:`orm_declarative_native_dataclasses`,\n specifies a default-value generation function that will take place\n as part of the ``__init__()``\n method as generated by the dataclass process.\n\n .. seealso::\n\n :ref:`defaults_default_factory_insert_default`\n\n :paramref:`_orm.mapped_column.default`\n\n :paramref:`_orm.mapped_column.insert_default`\n\n:param compare: Specific to\n :ref:`orm_declarative_native_dataclasses`, indicates if this field\n should be included in comparison operations when generating the\n ``__eq__()`` and ``__ne__()`` methods for the mapped class.\n\n .. versionadded:: 2.0.0b4\n\n:param kw_only: Specific to\n :ref:`orm_declarative_native_dataclasses`, indicates if this field\n should be marked as keyword-only when generating the ``__init__()``.\n\n:param hash: Specific to\n :ref:`orm_declarative_native_dataclasses`, controls if this field\n is included when generating the ``__hash__()`` method for the mapped\n class.\n\n .. versionadded:: 2.0.36\n\n:param dataclass_metadata: Specific to\n :ref:`orm_declarative_native_dataclasses`, supplies metadata\n to be attached to the generated dataclass field.\n\n .. versionadded:: 2.0.42\n\n:param \\**kw: All remaining keyword arguments are passed through to the\n constructor for the :class:`_schema.Column`.\n"}, 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