prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Fix warning in fast_neural_style example
  Here is another good first issue to improve the ignite project. Currently, we have a warning on this line: https://github.com/pytorch/ignite/blob/master/examples/fast_neural_style/vgg.py#L10 (fast neural style example)
   /opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
    warnings.warn(
  /opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `examples/fast_neural_style/vgg.py`
  Content:
  ```
  1 from collections import namedtuple
  2 
  3 import torch
  4 from torchvision import models
  5 
  6 
  7 class Vgg16(torch.nn.Module):
  8     def __init__(self, requires_grad=False):
  9         super(Vgg16, self).__init__()
  10         vgg_pretrained_features = models.vgg16(pretrained=True).features
  11         self.slice1 = torch.nn.Sequential()
  12         self.slice2 = torch.nn.Sequential()
  13         self.slice3 = torch.nn.Sequential()
  14         self.slice4 = torch.nn.Sequential()
  15         for x in range(4):
  16             self.slice1.add_module(str(x), vgg_pretrained_features[x])
  17         for x in range(4, 9):
  18             self.slice2.add_module(str(x), vgg_pretrained_features[x])
  19         for x in range(9, 16):
  20             self.slice3.add_module(str(x), vgg_pretrained_features[x])
  21         for x in range(16, 23):
  22             self.slice4.add_module(str(x), vgg_pretrained_features[x])
  23         if not requires_grad:
  24             for param in self.parameters():
  25                 param.requires_grad = False
  26 
  27     def forward(self, X):
  28         h = self.slice1(X)
  29         h_relu1_2 = h
  30         h = self.slice2(h)
  31         h_relu2_2 = h
  32         h = self.slice3(h)
  33         h_relu3_3 = h
  34         h = self.slice4(h)
  35         h_relu4_3 = h
  36         vgg_outputs = namedtuple("VggOutputs", ["relu1_2", "relu2_2", "relu3_3", "relu4_3"])
  37         out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3)
  38         return out
  39
  ```
  --- 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/examples/fast_neural_style/vgg.py b/examples/fast_neural_style/vgg.py
  --- a/examples/fast_neural_style/vgg.py
  +++ b/examples/fast_neural_style/vgg.py
  @@ -2,12 +2,13 @@
   
   import torch
   from torchvision import models
  +from torchvision.models.vgg import VGG16_Weights
   
   
   class Vgg16(torch.nn.Module):
       def __init__(self, requires_grad=False):
           super(Vgg16, self).__init__()
  -        vgg_pretrained_features = models.vgg16(pretrained=True).features
  +        vgg_pretrained_features = models.vgg16(weights=VGG16_Weights.IMAGENET1K_V1).features
           self.slice1 = torch.nn.Sequential()
           self.slice2 = torch.nn.Sequential()
           self.slice3 = torch.nn.Sequential()
  
verification_info: {"golden_diff": "diff --git a/examples/fast_neural_style/vgg.py b/examples/fast_neural_style/vgg.py\n--- a/examples/fast_neural_style/vgg.py\n+++ b/examples/fast_neural_style/vgg.py\n@@ -2,12 +2,13 @@\n \n import torch\n from torchvision import models\n+from torchvision.models.vgg import VGG16_Weights\n \n \n class Vgg16(torch.nn.Module):\n     def __init__(self, requires_grad=False):\n         super(Vgg16, self).__init__()\n-        vgg_pretrained_features = models.vgg16(pretrained=True).features\n+        vgg_pretrained_features = models.vgg16(weights=VGG16_Weights.IMAGENET1K_V1).features\n         self.slice1 = torch.nn.Sequential()\n         self.slice2 = torch.nn.Sequential()\n         self.slice3 = torch.nn.Sequential()\n", "issue": "Fix warning in fast_neural_style example\nHere is another good first issue to improve the ignite project. Currently, we have a warning on this line: https://github.com/pytorch/ignite/blob/master/examples/fast_neural_style/vgg.py#L10 (fast neural style example)\r\n /opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\r\n  warnings.warn(\r\n/opt/hostedtoolcache/Python/3.9.17/x64/lib/python3.9/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n", "before_files": [{"content": "from collections import namedtuple\n\nimport torch\nfrom torchvision import models\n\n\nclass Vgg16(torch.nn.Module):\n    def __init__(self, requires_grad=False):\n        super(Vgg16, self).__init__()\n        vgg_pretrained_features = models.vgg16(pretrained=True).features\n        self.slice1 = torch.nn.Sequential()\n        self.slice2 = torch.nn.Sequential()\n        self.slice3 = torch.nn.Sequential()\n        self.slice4 = torch.nn.Sequential()\n        for x in range(4):\n            self.slice1.add_module(str(x), vgg_pretrained_features[x])\n        for x in range(4, 9):\n            self.slice2.add_module(str(x), vgg_pretrained_features[x])\n        for x in range(9, 16):\n            self.slice3.add_module(str(x), vgg_pretrained_features[x])\n        for x in range(16, 23):\n            self.slice4.add_module(str(x), vgg_pretrained_features[x])\n        if not requires_grad:\n            for param in self.parameters():\n                param.requires_grad = False\n\n    def forward(self, X):\n        h = self.slice1(X)\n        h_relu1_2 = h\n        h = self.slice2(h)\n        h_relu2_2 = h\n        h = self.slice3(h)\n        h_relu3_3 = h\n        h = self.slice4(h)\n        h_relu4_3 = h\n        vgg_outputs = namedtuple(\"VggOutputs\", [\"relu1_2\", \"relu2_2\", \"relu3_3\", \"relu4_3\"])\n        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3)\n        return out\n", "path": "examples/fast_neural_style/vgg.py"}], "after_files": [{"content": "from collections import namedtuple\n\nimport torch\nfrom torchvision import models\nfrom torchvision.models.vgg import VGG16_Weights\n\n\nclass Vgg16(torch.nn.Module):\n    def __init__(self, requires_grad=False):\n        super(Vgg16, self).__init__()\n        vgg_pretrained_features = models.vgg16(weights=VGG16_Weights.IMAGENET1K_V1).features\n        self.slice1 = torch.nn.Sequential()\n        self.slice2 = torch.nn.Sequential()\n        self.slice3 = torch.nn.Sequential()\n        self.slice4 = torch.nn.Sequential()\n        for x in range(4):\n            self.slice1.add_module(str(x), vgg_pretrained_features[x])\n        for x in range(4, 9):\n            self.slice2.add_module(str(x), vgg_pretrained_features[x])\n        for x in range(9, 16):\n            self.slice3.add_module(str(x), vgg_pretrained_features[x])\n        for x in range(16, 23):\n            self.slice4.add_module(str(x), vgg_pretrained_features[x])\n        if not requires_grad:\n            for param in self.parameters():\n                param.requires_grad = False\n\n    def forward(self, X):\n        h = self.slice1(X)\n        h_relu1_2 = h\n        h = self.slice2(h)\n        h_relu2_2 = h\n        h = self.slice3(h)\n        h_relu3_3 = h\n        h = self.slice4(h)\n        h_relu4_3 = h\n        vgg_outputs = namedtuple(\"VggOutputs\", [\"relu1_2\", \"relu2_2\", \"relu3_3\", \"relu4_3\"])\n        out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3)\n        return out\n", "path": "examples/fast_neural_style/vgg.py"}]}