We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Add `save_model` arg to `mnist_hogwild` example
Currently the example doesn't support the `--save_model` argument like the other examples


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `mnist_hogwild/main.py`
Content:
```
1 from __future__ import print_function
2 import argparse
3 import torch
4 import torch.nn as nn
5 import torch.nn.functional as F
6 import torch.multiprocessing as mp
7 from torch.utils.data.sampler import Sampler
8 from torchvision import datasets, transforms
9 
10 from train import train, test
11 
12 # Training settings
13 parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
14 parser.add_argument('--batch-size', type=int, default=64, metavar='N',
15                     help='input batch size for training (default: 64)')
16 parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
17                     help='input batch size for testing (default: 1000)')
18 parser.add_argument('--epochs', type=int, default=10, metavar='N',
19                     help='number of epochs to train (default: 10)')
20 parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
21                     help='learning rate (default: 0.01)')
22 parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
23                     help='SGD momentum (default: 0.5)')
24 parser.add_argument('--seed', type=int, default=1, metavar='S',
25                     help='random seed (default: 1)')
26 parser.add_argument('--log-interval', type=int, default=10, metavar='N',
27                     help='how many batches to wait before logging training status')
28 parser.add_argument('--num-processes', type=int, default=2, metavar='N',
29                     help='how many training processes to use (default: 2)')
30 parser.add_argument('--cuda', action='store_true', default=False,
31                     help='enables CUDA training')
32 parser.add_argument('--mps', action='store_true', default=False,
33                         help='enables macOS GPU training')
34 parser.add_argument('--dry-run', action='store_true', default=False,
35                     help='quickly check a single pass')
36 
37 class Net(nn.Module):
38     def __init__(self):
39         super(Net, self).__init__()
40         self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
41         self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
42         self.conv2_drop = nn.Dropout2d()
43         self.fc1 = nn.Linear(320, 50)
44         self.fc2 = nn.Linear(50, 10)
45 
46     def forward(self, x):
47         x = F.relu(F.max_pool2d(self.conv1(x), 2))
48         x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
49         x = x.view(-1, 320)
50         x = F.relu(self.fc1(x))
51         x = F.dropout(x, training=self.training)
52         x = self.fc2(x)
53         return F.log_softmax(x, dim=1)
54 
55 
56 if __name__ == '__main__':
57     args = parser.parse_args()
58 
59     use_cuda = args.cuda and torch.cuda.is_available()
60     use_mps = args.mps and torch.backends.mps.is_available()
61     if use_cuda:
62         device = torch.device("cuda")
63     elif use_mps:
64         device = torch.device("mps")
65     else:
66         device = torch.device("cpu")
67 
68     transform=transforms.Compose([
69         transforms.ToTensor(),
70         transforms.Normalize((0.1307,), (0.3081,))
71         ])
72     dataset1 = datasets.MNIST('../data', train=True, download=True,
73                        transform=transform)
74     dataset2 = datasets.MNIST('../data', train=False,
75                        transform=transform)
76     kwargs = {'batch_size': args.batch_size,
77               'shuffle': True}
78     if use_cuda:
79         kwargs.update({'num_workers': 1,
80                        'pin_memory': True,
81                       })
82 
83     torch.manual_seed(args.seed)
84     mp.set_start_method('spawn', force=True)
85 
86     model = Net().to(device)
87     model.share_memory() # gradients are allocated lazily, so they are not shared here
88 
89     processes = []
90     for rank in range(args.num_processes):
91         p = mp.Process(target=train, args=(rank, args, model, device,
92                                            dataset1, kwargs))
93         # We first train the model across `num_processes` processes
94         p.start()
95         processes.append(p)
96     for p in processes:
97         p.join()
98 
99     # Once training is complete, we can test the model
100     test(args, model, device, dataset2, kwargs)
101
```
--- 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()

```

diff --git a/mnist_hogwild/main.py b/mnist_hogwild/main.py
--- a/mnist_hogwild/main.py
+++ b/mnist_hogwild/main.py
@@ -30,7 +30,9 @@
 parser.add_argument('--cuda', action='store_true', default=False,
                     help='enables CUDA training')
 parser.add_argument('--mps', action='store_true', default=False,
-                        help='enables macOS GPU training')
+                    help='enables macOS GPU training')
+parser.add_argument('--save_model', action='store_true', default=False,
+                    help='save the trained model to state_dict')
 parser.add_argument('--dry-run', action='store_true', default=False,
                     help='quickly check a single pass')
 
@@ -96,5 +98,8 @@
     for p in processes:
         p.join()
 
+    if args.save_model:
+        torch.save(model.state_dict(), "MNIST_hogwild.pt")
+
     # Once training is complete, we can test the model
     test(args, model, device, dataset2, kwargs)

{"golden_diff": "diff --git a/mnist_hogwild/main.py b/mnist_hogwild/main.py\n--- a/mnist_hogwild/main.py\n+++ b/mnist_hogwild/main.py\n@@ -30,7 +30,9 @@\n parser.add_argument('--cuda', action='store_true', default=False,\n                     help='enables CUDA training')\n parser.add_argument('--mps', action='store_true', default=False,\n-                        help='enables macOS GPU training')\n+                    help='enables macOS GPU training')\n+parser.add_argument('--save_model', action='store_true', default=False,\n+                    help='save the trained model to state_dict')\n parser.add_argument('--dry-run', action='store_true', default=False,\n                     help='quickly check a single pass')\n \n@@ -96,5 +98,8 @@\n     for p in processes:\n         p.join()\n \n+    if args.save_model:\n+        torch.save(model.state_dict(), \"MNIST_hogwild.pt\")\n+\n     # Once training is complete, we can test the model\n     test(args, model, device, dataset2, kwargs)\n", "issue": "Add `save_model` arg to `mnist_hogwild` example\nCurrently the example doesn't support the `--save_model` argument like the other examples\r\n\n", "before_files": [{"content": "from __future__ import print_function\nimport argparse\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.multiprocessing as mp\nfrom torch.utils.data.sampler import Sampler\nfrom torchvision import datasets, transforms\n\nfrom train import train, test\n\n# Training settings\nparser = argparse.ArgumentParser(description='PyTorch MNIST Example')\nparser.add_argument('--batch-size', type=int, default=64, metavar='N',\n                    help='input batch size for training (default: 64)')\nparser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',\n                    help='input batch size for testing (default: 1000)')\nparser.add_argument('--epochs', type=int, default=10, metavar='N',\n                    help='number of epochs to train (default: 10)')\nparser.add_argument('--lr', type=float, default=0.01, metavar='LR',\n                    help='learning rate (default: 0.01)')\nparser.add_argument('--momentum', type=float, default=0.5, metavar='M',\n                    help='SGD momentum (default: 0.5)')\nparser.add_argument('--seed', type=int, default=1, metavar='S',\n                    help='random seed (default: 1)')\nparser.add_argument('--log-interval', type=int, default=10, metavar='N',\n                    help='how many batches to wait before logging training status')\nparser.add_argument('--num-processes', type=int, default=2, metavar='N',\n                    help='how many training processes to use (default: 2)')\nparser.add_argument('--cuda', action='store_true', default=False,\n                    help='enables CUDA training')\nparser.add_argument('--mps', action='store_true', default=False,\n                        help='enables macOS GPU training')\nparser.add_argument('--dry-run', action='store_true', default=False,\n                    help='quickly check a single pass')\n\nclass Net(nn.Module):\n    def __init__(self):\n        super(Net, self).__init__()\n        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n        self.conv2_drop = nn.Dropout2d()\n        self.fc1 = nn.Linear(320, 50)\n        self.fc2 = nn.Linear(50, 10)\n\n    def forward(self, x):\n        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n        x = x.view(-1, 320)\n        x = F.relu(self.fc1(x))\n        x = F.dropout(x, training=self.training)\n        x = self.fc2(x)\n        return F.log_softmax(x, dim=1)\n\n\nif __name__ == '__main__':\n    args = parser.parse_args()\n\n    use_cuda = args.cuda and torch.cuda.is_available()\n    use_mps = args.mps and torch.backends.mps.is_available()\n    if use_cuda:\n        device = torch.device(\"cuda\")\n    elif use_mps:\n        device = torch.device(\"mps\")\n    else:\n        device = torch.device(\"cpu\")\n\n    transform=transforms.Compose([\n        transforms.ToTensor(),\n        transforms.Normalize((0.1307,), (0.3081,))\n        ])\n    dataset1 = datasets.MNIST('../data', train=True, download=True,\n                       transform=transform)\n    dataset2 = datasets.MNIST('../data', train=False,\n                       transform=transform)\n    kwargs = {'batch_size': args.batch_size,\n              'shuffle': True}\n    if use_cuda:\n        kwargs.update({'num_workers': 1,\n                       'pin_memory': True,\n                      })\n\n    torch.manual_seed(args.seed)\n    mp.set_start_method('spawn', force=True)\n\n    model = Net().to(device)\n    model.share_memory() # gradients are allocated lazily, so they are not shared here\n\n    processes = []\n    for rank in range(args.num_processes):\n        p = mp.Process(target=train, args=(rank, args, model, device,\n                                           dataset1, kwargs))\n        # We first train the model across `num_processes` processes\n        p.start()\n        processes.append(p)\n    for p in processes:\n        p.join()\n\n    # Once training is complete, we can test the model\n    test(args, model, device, dataset2, kwargs)\n", "path": "mnist_hogwild/main.py"}], "after_files": [{"content": "from __future__ import print_function\nimport argparse\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.multiprocessing as mp\nfrom torch.utils.data.sampler import Sampler\nfrom torchvision import datasets, transforms\n\nfrom train import train, test\n\n# Training settings\nparser = argparse.ArgumentParser(description='PyTorch MNIST Example')\nparser.add_argument('--batch-size', type=int, default=64, metavar='N',\n                    help='input batch size for training (default: 64)')\nparser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',\n                    help='input batch size for testing (default: 1000)')\nparser.add_argument('--epochs', type=int, default=10, metavar='N',\n                    help='number of epochs to train (default: 10)')\nparser.add_argument('--lr', type=float, default=0.01, metavar='LR',\n                    help='learning rate (default: 0.01)')\nparser.add_argument('--momentum', type=float, default=0.5, metavar='M',\n                    help='SGD momentum (default: 0.5)')\nparser.add_argument('--seed', type=int, default=1, metavar='S',\n                    help='random seed (default: 1)')\nparser.add_argument('--log-interval', type=int, default=10, metavar='N',\n                    help='how many batches to wait before logging training status')\nparser.add_argument('--num-processes', type=int, default=2, metavar='N',\n                    help='how many training processes to use (default: 2)')\nparser.add_argument('--cuda', action='store_true', default=False,\n                    help='enables CUDA training')\nparser.add_argument('--mps', action='store_true', default=False,\n                    help='enables macOS GPU training')\nparser.add_argument('--save_model', action='store_true', default=False,\n                    help='save the trained model to state_dict')\nparser.add_argument('--dry-run', action='store_true', default=False,\n                    help='quickly check a single pass')\n\nclass Net(nn.Module):\n    def __init__(self):\n        super(Net, self).__init__()\n        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)\n        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)\n        self.conv2_drop = nn.Dropout2d()\n        self.fc1 = nn.Linear(320, 50)\n        self.fc2 = nn.Linear(50, 10)\n\n    def forward(self, x):\n        x = F.relu(F.max_pool2d(self.conv1(x), 2))\n        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))\n        x = x.view(-1, 320)\n        x = F.relu(self.fc1(x))\n        x = F.dropout(x, training=self.training)\n        x = self.fc2(x)\n        return F.log_softmax(x, dim=1)\n\n\nif __name__ == '__main__':\n    args = parser.parse_args()\n\n    use_cuda = args.cuda and torch.cuda.is_available()\n    use_mps = args.mps and torch.backends.mps.is_available()\n    if use_cuda:\n        device = torch.device(\"cuda\")\n    elif use_mps:\n        device = torch.device(\"mps\")\n    else:\n        device = torch.device(\"cpu\")\n\n    transform=transforms.Compose([\n        transforms.ToTensor(),\n        transforms.Normalize((0.1307,), (0.3081,))\n        ])\n    dataset1 = datasets.MNIST('../data', train=True, download=True,\n                       transform=transform)\n    dataset2 = datasets.MNIST('../data', train=False,\n                       transform=transform)\n    kwargs = {'batch_size': args.batch_size,\n              'shuffle': True}\n    if use_cuda:\n        kwargs.update({'num_workers': 1,\n                       'pin_memory': True,\n                      })\n\n    torch.manual_seed(args.seed)\n    mp.set_start_method('spawn', force=True)\n\n    model = Net().to(device)\n    model.share_memory() # gradients are allocated lazily, so they are not shared here\n\n    processes = []\n    for rank in range(args.num_processes):\n        p = mp.Process(target=train, args=(rank, args, model, device,\n                                           dataset1, kwargs))\n        # We first train the model across `num_processes` processes\n        p.start()\n        processes.append(p)\n    for p in processes:\n        p.join()\n\n    if args.save_model:\n        torch.save(model.state_dict(), \"MNIST_hogwild.pt\")\n\n    # Once training is complete, we can test the model\n    test(args, model, device, dataset2, kwargs)\n", "path": "mnist_hogwild/main.py"}]}