prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Mypy job failing due to incorrect return-value from upstream
  The latest, freshest mypy time waster
  
  https://app.circleci.com/pipelines/github/pytorch/vision/17366/workflows/a475b8b1-1463-4159-bf00-be2d82030c0b/jobs/1405650
  
  Caused by https://github.com/pytorch/pytorch/pull/74171
  
  I think we just need to wait for https://github.com/pytorch/pytorch/pull/76895 to be in the nightlies
  
  ```
  torchvision/models/_api.py:64: error: Incompatible return value type (got
  Module, expected "OrderedDict[Any, Any]")  [return-value]
              return load_state_dict_from_url(self.url, progress=progress)
                     ^
  Found 1 error in 1 file (checked 209 source files)
  
  Exited with code exit status 1
  
  CircleCI received exit code 1
  ```
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `torchvision/models/_api.py`
  Content:
  ```
  1 import importlib
  2 import inspect
  3 import sys
  4 from collections import OrderedDict
  5 from dataclasses import dataclass, fields
  6 from inspect import signature
  7 from typing import Any, Callable, Dict, cast
  8 
  9 from torchvision._utils import StrEnum
  10 
  11 from .._internally_replaced_utils import load_state_dict_from_url
  12 
  13 
  14 __all__ = ["WeightsEnum", "Weights", "get_weight"]
  15 
  16 
  17 @dataclass
  18 class Weights:
  19     """
  20     This class is used to group important attributes associated with the pre-trained weights.
  21 
  22     Args:
  23         url (str): The location where we find the weights.
  24         transforms (Callable): A callable that constructs the preprocessing method (or validation preset transforms)
  25             needed to use the model. The reason we attach a constructor method rather than an already constructed
  26             object is because the specific object might have memory and thus we want to delay initialization until
  27             needed.
  28         meta (Dict[str, Any]): Stores meta-data related to the weights of the model and its configuration. These can be
  29             informative attributes (for example the number of parameters/flops, recipe link/methods used in training
  30             etc), configuration parameters (for example the `num_classes`) needed to construct the model or important
  31             meta-data (for example the `classes` of a classification model) needed to use the model.
  32     """
  33 
  34     url: str
  35     transforms: Callable
  36     meta: Dict[str, Any]
  37 
  38 
  39 class WeightsEnum(StrEnum):
  40     """
  41     This class is the parent class of all model weights. Each model building method receives an optional `weights`
  42     parameter with its associated pre-trained weights. It inherits from `Enum` and its values should be of type
  43     `Weights`.
  44 
  45     Args:
  46         value (Weights): The data class entry with the weight information.
  47     """
  48 
  49     def __init__(self, value: Weights):
  50         self._value_ = value
  51 
  52     @classmethod
  53     def verify(cls, obj: Any) -> Any:
  54         if obj is not None:
  55             if type(obj) is str:
  56                 obj = cls.from_str(obj.replace(cls.__name__ + ".", ""))
  57             elif not isinstance(obj, cls):
  58                 raise TypeError(
  59                     f"Invalid Weight class provided; expected {cls.__name__} but received {obj.__class__.__name__}."
  60                 )
  61         return obj
  62 
  63     def get_state_dict(self, progress: bool) -> OrderedDict:
  64         return load_state_dict_from_url(self.url, progress=progress)
  65 
  66     def __repr__(self) -> str:
  67         return f"{self.__class__.__name__}.{self._name_}"
  68 
  69     def __getattr__(self, name):
  70         # Be able to fetch Weights attributes directly
  71         for f in fields(Weights):
  72             if f.name == name:
  73                 return object.__getattribute__(self.value, name)
  74         return super().__getattr__(name)
  75 
  76 
  77 def get_weight(name: str) -> WeightsEnum:
  78     """
  79     Gets the weight enum value by its full name. Example: "ResNet50_Weights.IMAGENET1K_V1"
  80 
  81     Args:
  82         name (str): The name of the weight enum entry.
  83 
  84     Returns:
  85         WeightsEnum: The requested weight enum.
  86     """
  87     try:
  88         enum_name, value_name = name.split(".")
  89     except ValueError:
  90         raise ValueError(f"Invalid weight name provided: '{name}'.")
  91 
  92     base_module_name = ".".join(sys.modules[__name__].__name__.split(".")[:-1])
  93     base_module = importlib.import_module(base_module_name)
  94     model_modules = [base_module] + [
  95         x[1] for x in inspect.getmembers(base_module, inspect.ismodule) if x[1].__file__.endswith("__init__.py")
  96     ]
  97 
  98     weights_enum = None
  99     for m in model_modules:
  100         potential_class = m.__dict__.get(enum_name, None)
  101         if potential_class is not None and issubclass(potential_class, WeightsEnum):
  102             weights_enum = potential_class
  103             break
  104 
  105     if weights_enum is None:
  106         raise ValueError(f"The weight enum '{enum_name}' for the specific method couldn't be retrieved.")
  107 
  108     return weights_enum.from_str(value_name)
  109 
  110 
  111 def get_enum_from_fn(fn: Callable) -> WeightsEnum:
  112     """
  113     Internal method that gets the weight enum of a specific model builder method.
  114     Might be removed after the handle_legacy_interface is removed.
  115 
  116     Args:
  117         fn (Callable): The builder method used to create the model.
  118         weight_name (str): The name of the weight enum entry of the specific model.
  119     Returns:
  120         WeightsEnum: The requested weight enum.
  121     """
  122     sig = signature(fn)
  123     if "weights" not in sig.parameters:
  124         raise ValueError("The method is missing the 'weights' argument.")
  125 
  126     ann = signature(fn).parameters["weights"].annotation
  127     weights_enum = None
  128     if isinstance(ann, type) and issubclass(ann, WeightsEnum):
  129         weights_enum = ann
  130     else:
  131         # handle cases like Union[Optional, T]
  132         # TODO: Replace ann.__args__ with typing.get_args(ann) after python >= 3.8
  133         for t in ann.__args__:  # type: ignore[union-attr]
  134             if isinstance(t, type) and issubclass(t, WeightsEnum):
  135                 weights_enum = t
  136                 break
  137 
  138     if weights_enum is None:
  139         raise ValueError(
  140             "The WeightsEnum class for the specific method couldn't be retrieved. Make sure the typing info is correct."
  141         )
  142 
  143     return cast(WeightsEnum, weights_enum)
  144
  ```
  --- END FILES ---
  
  Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.
  
  Here is an example:
  
  ```diff
  diff --git a/examples/server_async.py b/examples/server_async.py
  --- a/examples/server_async.py
  +++ b/examples/server_async.py
  @@ -313,4 +313,4 @@
   
   
   if __name__ == "__main__":
  -    asyncio.run(run_async_server("."), debug=True)
  +    asyncio.run(run_async_server(), debug=True)
  diff --git a/examples/server_sync.py b/examples/server_sync.py
  --- a/examples/server_sync.py
  +++ b/examples/server_sync.py
  @@ -313,5 +313,5 @@
   
   
   if __name__ == "__main__":
  -    server = run_sync_server(".")
  +    server = run_sync_server()
       server.shutdown()
  
  ```
  
golden_diff: diff --git a/torchvision/models/_api.py b/torchvision/models/_api.py
  --- a/torchvision/models/_api.py
  +++ b/torchvision/models/_api.py
  @@ -1,7 +1,6 @@
   import importlib
   import inspect
   import sys
  -from collections import OrderedDict
   from dataclasses import dataclass, fields
   from inspect import signature
   from typing import Any, Callable, Dict, cast
  @@ -60,7 +59,7 @@
                   )
           return obj
   
  -    def get_state_dict(self, progress: bool) -> OrderedDict:
  +    def get_state_dict(self, progress: bool) -> Dict[str, Any]:
           return load_state_dict_from_url(self.url, progress=progress)
   
       def __repr__(self) -> str:
  
verification_info: {"golden_diff": "diff --git a/torchvision/models/_api.py b/torchvision/models/_api.py\n--- a/torchvision/models/_api.py\n+++ b/torchvision/models/_api.py\n@@ -1,7 +1,6 @@\n import importlib\n import inspect\n import sys\n-from collections import OrderedDict\n from dataclasses import dataclass, fields\n from inspect import signature\n from typing import Any, Callable, Dict, cast\n@@ -60,7 +59,7 @@\n                 )\n         return obj\n \n-    def get_state_dict(self, progress: bool) -> OrderedDict:\n+    def get_state_dict(self, progress: bool) -> Dict[str, Any]:\n         return load_state_dict_from_url(self.url, progress=progress)\n \n     def __repr__(self) -> str:\n", "issue": "Mypy job failing due to incorrect return-value from upstream\nThe latest, freshest mypy time waster\r\n\r\nhttps://app.circleci.com/pipelines/github/pytorch/vision/17366/workflows/a475b8b1-1463-4159-bf00-be2d82030c0b/jobs/1405650\r\n\r\nCaused by https://github.com/pytorch/pytorch/pull/74171\r\n\r\nI think we just need to wait for https://github.com/pytorch/pytorch/pull/76895 to be in the nightlies\r\n\r\n```\r\ntorchvision/models/_api.py:64: error: Incompatible return value type (got\r\nModule, expected \"OrderedDict[Any, Any]\")  [return-value]\r\n            return load_state_dict_from_url(self.url, progress=progress)\r\n                   ^\r\nFound 1 error in 1 file (checked 209 source files)\r\n\r\nExited with code exit status 1\r\n\r\nCircleCI received exit code 1\r\n```\n", "before_files": [{"content": "import importlib\nimport inspect\nimport sys\nfrom collections import OrderedDict\nfrom dataclasses import dataclass, fields\nfrom inspect import signature\nfrom typing import Any, Callable, Dict, cast\n\nfrom torchvision._utils import StrEnum\n\nfrom .._internally_replaced_utils import load_state_dict_from_url\n\n\n__all__ = [\"WeightsEnum\", \"Weights\", \"get_weight\"]\n\n\n@dataclass\nclass Weights:\n    \"\"\"\n    This class is used to group important attributes associated with the pre-trained weights.\n\n    Args:\n        url (str): The location where we find the weights.\n        transforms (Callable): A callable that constructs the preprocessing method (or validation preset transforms)\n            needed to use the model. The reason we attach a constructor method rather than an already constructed\n            object is because the specific object might have memory and thus we want to delay initialization until\n            needed.\n        meta (Dict[str, Any]): Stores meta-data related to the weights of the model and its configuration. These can be\n            informative attributes (for example the number of parameters/flops, recipe link/methods used in training\n            etc), configuration parameters (for example the `num_classes`) needed to construct the model or important\n            meta-data (for example the `classes` of a classification model) needed to use the model.\n    \"\"\"\n\n    url: str\n    transforms: Callable\n    meta: Dict[str, Any]\n\n\nclass WeightsEnum(StrEnum):\n    \"\"\"\n    This class is the parent class of all model weights. Each model building method receives an optional `weights`\n    parameter with its associated pre-trained weights. It inherits from `Enum` and its values should be of type\n    `Weights`.\n\n    Args:\n        value (Weights): The data class entry with the weight information.\n    \"\"\"\n\n    def __init__(self, value: Weights):\n        self._value_ = value\n\n    @classmethod\n    def verify(cls, obj: Any) -> Any:\n        if obj is not None:\n            if type(obj) is str:\n                obj = cls.from_str(obj.replace(cls.__name__ + \".\", \"\"))\n            elif not isinstance(obj, cls):\n                raise TypeError(\n                    f\"Invalid Weight class provided; expected {cls.__name__} but received {obj.__class__.__name__}.\"\n                )\n        return obj\n\n    def get_state_dict(self, progress: bool) -> OrderedDict:\n        return load_state_dict_from_url(self.url, progress=progress)\n\n    def __repr__(self) -> str:\n        return f\"{self.__class__.__name__}.{self._name_}\"\n\n    def __getattr__(self, name):\n        # Be able to fetch Weights attributes directly\n        for f in fields(Weights):\n            if f.name == name:\n                return object.__getattribute__(self.value, name)\n        return super().__getattr__(name)\n\n\ndef get_weight(name: str) -> WeightsEnum:\n    \"\"\"\n    Gets the weight enum value by its full name. Example: \"ResNet50_Weights.IMAGENET1K_V1\"\n\n    Args:\n        name (str): The name of the weight enum entry.\n\n    Returns:\n        WeightsEnum: The requested weight enum.\n    \"\"\"\n    try:\n        enum_name, value_name = name.split(\".\")\n    except ValueError:\n        raise ValueError(f\"Invalid weight name provided: '{name}'.\")\n\n    base_module_name = \".\".join(sys.modules[__name__].__name__.split(\".\")[:-1])\n    base_module = importlib.import_module(base_module_name)\n    model_modules = [base_module] + [\n        x[1] for x in inspect.getmembers(base_module, inspect.ismodule) if x[1].__file__.endswith(\"__init__.py\")\n    ]\n\n    weights_enum = None\n    for m in model_modules:\n        potential_class = m.__dict__.get(enum_name, None)\n        if potential_class is not None and issubclass(potential_class, WeightsEnum):\n            weights_enum = potential_class\n            break\n\n    if weights_enum is None:\n        raise ValueError(f\"The weight enum '{enum_name}' for the specific method couldn't be retrieved.\")\n\n    return weights_enum.from_str(value_name)\n\n\ndef get_enum_from_fn(fn: Callable) -> WeightsEnum:\n    \"\"\"\n    Internal method that gets the weight enum of a specific model builder method.\n    Might be removed after the handle_legacy_interface is removed.\n\n    Args:\n        fn (Callable): The builder method used to create the model.\n        weight_name (str): The name of the weight enum entry of the specific model.\n    Returns:\n        WeightsEnum: The requested weight enum.\n    \"\"\"\n    sig = signature(fn)\n    if \"weights\" not in sig.parameters:\n        raise ValueError(\"The method is missing the 'weights' argument.\")\n\n    ann = signature(fn).parameters[\"weights\"].annotation\n    weights_enum = None\n    if isinstance(ann, type) and issubclass(ann, WeightsEnum):\n        weights_enum = ann\n    else:\n        # handle cases like Union[Optional, T]\n        # TODO: Replace ann.__args__ with typing.get_args(ann) after python >= 3.8\n        for t in ann.__args__:  # type: ignore[union-attr]\n            if isinstance(t, type) and issubclass(t, WeightsEnum):\n                weights_enum = t\n                break\n\n    if weights_enum is None:\n        raise ValueError(\n            \"The WeightsEnum class for the specific method couldn't be retrieved. Make sure the typing info is correct.\"\n        )\n\n    return cast(WeightsEnum, weights_enum)\n", "path": "torchvision/models/_api.py"}], "after_files": [{"content": "import importlib\nimport inspect\nimport sys\nfrom dataclasses import dataclass, fields\nfrom inspect import signature\nfrom typing import Any, Callable, Dict, cast\n\nfrom torchvision._utils import StrEnum\n\nfrom .._internally_replaced_utils import load_state_dict_from_url\n\n\n__all__ = [\"WeightsEnum\", \"Weights\", \"get_weight\"]\n\n\n@dataclass\nclass Weights:\n    \"\"\"\n    This class is used to group important attributes associated with the pre-trained weights.\n\n    Args:\n        url (str): The location where we find the weights.\n        transforms (Callable): A callable that constructs the preprocessing method (or validation preset transforms)\n            needed to use the model. The reason we attach a constructor method rather than an already constructed\n            object is because the specific object might have memory and thus we want to delay initialization until\n            needed.\n        meta (Dict[str, Any]): Stores meta-data related to the weights of the model and its configuration. These can be\n            informative attributes (for example the number of parameters/flops, recipe link/methods used in training\n            etc), configuration parameters (for example the `num_classes`) needed to construct the model or important\n            meta-data (for example the `classes` of a classification model) needed to use the model.\n    \"\"\"\n\n    url: str\n    transforms: Callable\n    meta: Dict[str, Any]\n\n\nclass WeightsEnum(StrEnum):\n    \"\"\"\n    This class is the parent class of all model weights. Each model building method receives an optional `weights`\n    parameter with its associated pre-trained weights. It inherits from `Enum` and its values should be of type\n    `Weights`.\n\n    Args:\n        value (Weights): The data class entry with the weight information.\n    \"\"\"\n\n    def __init__(self, value: Weights):\n        self._value_ = value\n\n    @classmethod\n    def verify(cls, obj: Any) -> Any:\n        if obj is not None:\n            if type(obj) is str:\n                obj = cls.from_str(obj.replace(cls.__name__ + \".\", \"\"))\n            elif not isinstance(obj, cls):\n                raise TypeError(\n                    f\"Invalid Weight class provided; expected {cls.__name__} but received {obj.__class__.__name__}.\"\n                )\n        return obj\n\n    def get_state_dict(self, progress: bool) -> Dict[str, Any]:\n        return load_state_dict_from_url(self.url, progress=progress)\n\n    def __repr__(self) -> str:\n        return f\"{self.__class__.__name__}.{self._name_}\"\n\n    def __getattr__(self, name):\n        # Be able to fetch Weights attributes directly\n        for f in fields(Weights):\n            if f.name == name:\n                return object.__getattribute__(self.value, name)\n        return super().__getattr__(name)\n\n\ndef get_weight(name: str) -> WeightsEnum:\n    \"\"\"\n    Gets the weight enum value by its full name. Example: \"ResNet50_Weights.IMAGENET1K_V1\"\n\n    Args:\n        name (str): The name of the weight enum entry.\n\n    Returns:\n        WeightsEnum: The requested weight enum.\n    \"\"\"\n    try:\n        enum_name, value_name = name.split(\".\")\n    except ValueError:\n        raise ValueError(f\"Invalid weight name provided: '{name}'.\")\n\n    base_module_name = \".\".join(sys.modules[__name__].__name__.split(\".\")[:-1])\n    base_module = importlib.import_module(base_module_name)\n    model_modules = [base_module] + [\n        x[1] for x in inspect.getmembers(base_module, inspect.ismodule) if x[1].__file__.endswith(\"__init__.py\")\n    ]\n\n    weights_enum = None\n    for m in model_modules:\n        potential_class = m.__dict__.get(enum_name, None)\n        if potential_class is not None and issubclass(potential_class, WeightsEnum):\n            weights_enum = potential_class\n            break\n\n    if weights_enum is None:\n        raise ValueError(f\"The weight enum '{enum_name}' for the specific method couldn't be retrieved.\")\n\n    return weights_enum.from_str(value_name)\n\n\ndef get_enum_from_fn(fn: Callable) -> WeightsEnum:\n    \"\"\"\n    Internal method that gets the weight enum of a specific model builder method.\n    Might be removed after the handle_legacy_interface is removed.\n\n    Args:\n        fn (Callable): The builder method used to create the model.\n        weight_name (str): The name of the weight enum entry of the specific model.\n    Returns:\n        WeightsEnum: The requested weight enum.\n    \"\"\"\n    sig = signature(fn)\n    if \"weights\" not in sig.parameters:\n        raise ValueError(\"The method is missing the 'weights' argument.\")\n\n    ann = signature(fn).parameters[\"weights\"].annotation\n    weights_enum = None\n    if isinstance(ann, type) and issubclass(ann, WeightsEnum):\n        weights_enum = ann\n    else:\n        # handle cases like Union[Optional, T]\n        # TODO: Replace ann.__args__ with typing.get_args(ann) after python >= 3.8\n        for t in ann.__args__:  # type: ignore[union-attr]\n            if isinstance(t, type) and issubclass(t, WeightsEnum):\n                weights_enum = t\n                break\n\n    if weights_enum is None:\n        raise ValueError(\n            \"The WeightsEnum class for the specific method couldn't be retrieved. Make sure the typing info is correct.\"\n        )\n\n    return cast(WeightsEnum, weights_enum)\n", "path": "torchvision/models/_api.py"}]}