[BE][CI][Easy] bump ruff to 0.9.0: long statements in docstrings (pytorch#146509)

Pull Request resolved: pytorch#146509
Approved by: https://github.com/justinchuby, https://github.com/Skylion007

Author: XuehaiPan

torch/hub.py

@@ -666,7 +666,11 @@ def _load_local(hubconf_dir, model, *args, **kwargs):
     Example:
         >>> # xdoctest: +SKIP("stub local path")
         >>> path = "/some/local/path/pytorch/vision"
-        >>> model = _load_local(path, "resnet50", weights="ResNet50_Weights.IMAGENET1K_V1")
+        >>> model = _load_local(
+        ...     path,
+        ...     "resnet50",
+        ...     weights="ResNet50_Weights.IMAGENET1K_V1",
+        ... )
     """
     with _add_to_sys_path(hubconf_dir):
         hubconf_path = os.path.join(hubconf_dir, MODULE_HUBCONF)

torch/onnx/_internal/fx/fx_symbolic_graph_extractor.py

@@ -153,7 +153,10 @@ def f(x):
                     return out
 
 
-                f = fx.symbolic_trace(f, concrete_args={"x": {"a": fx.PH, "b": fx.PH, "c": fx.PH}})
+                f = fx.symbolic_trace(
+                    f,
+                    concrete_args={"x": {"a": fx.PH, "b": fx.PH, "c": fx.PH}},
+                )
                 assert f({"a": 1, "b": 2, "c": 4}) == 7
     """
 

torch/onnx/_internal/fx/passes/modularization.py

@@ -822,7 +822,10 @@ class Modularize(_pass.Transform):
         ... )
         >>> gm.print_readable()
 
-        >>> gm = passes.Modularize(infra.DiagnosticContext("test_context", "1.0"), gm).run()
+        >>> gm = passes.Modularize(
+        ...     infra.DiagnosticContext("test_context", "1.0"),
+        ...     gm,
+        ... ).run()
         >>> gm.print_readable()
 
     """

torch/serialization.py

@@ -1322,10 +1322,16 @@ def load(
         >>> # xdoctest: +SKIP("undefined filepaths")
         >>> torch.load("tensors.pt", weights_only=True)
         # Load all tensors onto the CPU
-        >>> torch.load("tensors.pt", map_location=torch.device("cpu"), weights_only=True)
+        >>> torch.load(
+        ...     "tensors.pt",
+        ...     map_location=torch.device("cpu"),
+        ...     weights_only=True,
+        ... )
         # Load all tensors onto the CPU, using a function
         >>> torch.load(
-        ...     "tensors.pt", map_location=lambda storage, loc: storage, weights_only=True
+        ...     "tensors.pt",
+        ...     map_location=lambda storage, loc: storage,
+        ...     weights_only=True,
         ... )
         # Load all tensors onto GPU 1
         >>> torch.load(
@@ -1334,7 +1340,11 @@ def load(
         ...     weights_only=True,
         ... )  # type: ignore[attr-defined]
         # Map tensors from GPU 1 to GPU 0
-        >>> torch.load("tensors.pt", map_location={"cuda:1": "cuda:0"}, weights_only=True)
+        >>> torch.load(
+        ...     "tensors.pt",
+        ...     map_location={"cuda:1": "cuda:0"},
+        ...     weights_only=True,
+        ... )
         # Load tensor from io.BytesIO object
         # Loading from a buffer setting weights_only=False, warning this can be unsafe
         >>> with open("tensor.pt", "rb") as f: