We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
two lines of code in mnist/main.py
There are two arguments called batch_size and test_batch_size:
`parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                    help='input batch size for training (default: 64)')`
`parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                    help='input batch size for testing (default: 1000)')`
but batch_size is used here:
`test_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=False, transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=args.batch_size, shuffle=True, **kwargs)`

Also, what does this line(line 105) do:
`test_loss = test_loss`

and it seems that `epoch` is not used in test().

--- 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/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.optim as optim
7 from torchvision import datasets, transforms
8 from torch.optim.lr_scheduler import StepLR
9 
10 
11 class Net(nn.Module):
12     def __init__(self):
13         super(Net, self).__init__()
14         self.conv1 = nn.Conv2d(1, 32, 3, 1)
15         self.conv2 = nn.Conv2d(32, 64, 3, 1)
16         self.dropout1 = nn.Dropout2d(0.25)
17         self.dropout2 = nn.Dropout2d(0.5)
18         self.fc1 = nn.Linear(9216, 128)
19         self.fc2 = nn.Linear(128, 10)
20 
21     def forward(self, x):
22         x = self.conv1(x)
23         x = F.relu(x)
24         x = self.conv2(x)
25         x = F.relu(x)
26         x = F.max_pool2d(x, 2)
27         x = self.dropout1(x)
28         x = torch.flatten(x, 1)
29         x = self.fc1(x)
30         x = F.relu(x)
31         x = self.dropout2(x)
32         x = self.fc2(x)
33         output = F.log_softmax(x, dim=1)
34         return output
35 
36 
37 def train(args, model, device, train_loader, optimizer, epoch):
38     model.train()
39     for batch_idx, (data, target) in enumerate(train_loader):
40         data, target = data.to(device), target.to(device)
41         optimizer.zero_grad()
42         output = model(data)
43         loss = F.nll_loss(output, target)
44         loss.backward()
45         optimizer.step()
46         if batch_idx % args.log_interval == 0:
47             print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
48                 epoch, batch_idx * len(data), len(train_loader.dataset),
49                 100. * batch_idx / len(train_loader), loss.item()))
50             if args.dry_run:
51                 break
52 
53 
54 def test(model, device, test_loader):
55     model.eval()
56     test_loss = 0
57     correct = 0
58     with torch.no_grad():
59         for data, target in test_loader:
60             data, target = data.to(device), target.to(device)
61             output = model(data)
62             test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
63             pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
64             correct += pred.eq(target.view_as(pred)).sum().item()
65 
66     test_loss /= len(test_loader.dataset)
67 
68     print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
69         test_loss, correct, len(test_loader.dataset),
70         100. * correct / len(test_loader.dataset)))
71 
72 
73 def main():
74     # Training settings
75     parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
76     parser.add_argument('--batch-size', type=int, default=64, metavar='N',
77                         help='input batch size for training (default: 64)')
78     parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
79                         help='input batch size for testing (default: 1000)')
80     parser.add_argument('--epochs', type=int, default=14, metavar='N',
81                         help='number of epochs to train (default: 14)')
82     parser.add_argument('--lr', type=float, default=1.0, metavar='LR',
83                         help='learning rate (default: 1.0)')
84     parser.add_argument('--gamma', type=float, default=0.7, metavar='M',
85                         help='Learning rate step gamma (default: 0.7)')
86     parser.add_argument('--no-cuda', action='store_true', default=False,
87                         help='disables CUDA training')
88     parser.add_argument('--dry-run', action='store_true', default=False,
89                         help='quickly check a single pass')
90     parser.add_argument('--seed', type=int, default=1, metavar='S',
91                         help='random seed (default: 1)')
92     parser.add_argument('--log-interval', type=int, default=10, metavar='N',
93                         help='how many batches to wait before logging training status')
94     parser.add_argument('--save-model', action='store_true', default=False,
95                         help='For Saving the current Model')
96     args = parser.parse_args()
97     use_cuda = not args.no_cuda and torch.cuda.is_available()
98 
99     torch.manual_seed(args.seed)
100 
101     device = torch.device("cuda" if use_cuda else "cpu")
102 
103     kwargs = {'batch_size': args.batch_size}
104     if use_cuda:
105         kwargs.update({'num_workers': 1,
106                        'pin_memory': True,
107                        'shuffle': True},
108                      )
109 
110     transform=transforms.Compose([
111         transforms.ToTensor(),
112         transforms.Normalize((0.1307,), (0.3081,))
113         ])
114     dataset1 = datasets.MNIST('../data', train=True, download=True,
115                        transform=transform)
116     dataset2 = datasets.MNIST('../data', train=False,
117                        transform=transform)
118     train_loader = torch.utils.data.DataLoader(dataset1,**kwargs)
119     test_loader = torch.utils.data.DataLoader(dataset2, **kwargs)
120 
121     model = Net().to(device)
122     optimizer = optim.Adadelta(model.parameters(), lr=args.lr)
123 
124     scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
125     for epoch in range(1, args.epochs + 1):
126         train(args, model, device, train_loader, optimizer, epoch)
127         test(model, device, test_loader)
128         scheduler.step()
129 
130     if args.save_model:
131         torch.save(model.state_dict(), "mnist_cnn.pt")
132 
133 
134 if __name__ == '__main__':
135     main()
136
```
--- 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/main.py b/mnist/main.py
--- a/mnist/main.py
+++ b/mnist/main.py
@@ -100,12 +100,14 @@
 
     device = torch.device("cuda" if use_cuda else "cpu")
 
-    kwargs = {'batch_size': args.batch_size}
+    train_kwargs = {'batch_size': args.batch_size}
+    test_kwargs = {'batch_size': args.test_batch_size}
     if use_cuda:
-        kwargs.update({'num_workers': 1,
+        cuda_kwargs = {'num_workers': 1,
                        'pin_memory': True,
-                       'shuffle': True},
-                     )
+                       'shuffle': True}
+        train_kwargs.update(cuda_kwargs)
+        test_kwargs.update(cuda_kwargs)
 
     transform=transforms.Compose([
         transforms.ToTensor(),
@@ -115,8 +117,8 @@
                        transform=transform)
     dataset2 = datasets.MNIST('../data', train=False,
                        transform=transform)
-    train_loader = torch.utils.data.DataLoader(dataset1,**kwargs)
-    test_loader = torch.utils.data.DataLoader(dataset2, **kwargs)
+    train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs)
+    test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
 
     model = Net().to(device)
     optimizer = optim.Adadelta(model.parameters(), lr=args.lr)

{"golden_diff": "diff --git a/mnist/main.py b/mnist/main.py\n--- a/mnist/main.py\n+++ b/mnist/main.py\n@@ -100,12 +100,14 @@\n \n     device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n \n-    kwargs = {'batch_size': args.batch_size}\n+    train_kwargs = {'batch_size': args.batch_size}\n+    test_kwargs = {'batch_size': args.test_batch_size}\n     if use_cuda:\n-        kwargs.update({'num_workers': 1,\n+        cuda_kwargs = {'num_workers': 1,\n                        'pin_memory': True,\n-                       'shuffle': True},\n-                     )\n+                       'shuffle': True}\n+        train_kwargs.update(cuda_kwargs)\n+        test_kwargs.update(cuda_kwargs)\n \n     transform=transforms.Compose([\n         transforms.ToTensor(),\n@@ -115,8 +117,8 @@\n                        transform=transform)\n     dataset2 = datasets.MNIST('../data', train=False,\n                        transform=transform)\n-    train_loader = torch.utils.data.DataLoader(dataset1,**kwargs)\n-    test_loader = torch.utils.data.DataLoader(dataset2, **kwargs)\n+    train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs)\n+    test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)\n \n     model = Net().to(device)\n     optimizer = optim.Adadelta(model.parameters(), lr=args.lr)\n", "issue": "two lines of code in mnist/main.py\nThere are two arguments called batch_size and test_batch_size:\r\n`parser.add_argument('--batch-size', type=int, default=64, metavar='N',\r\n                    help='input batch size for training (default: 64)')`\r\n`parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',\r\n                    help='input batch size for testing (default: 1000)')`\r\nbut batch_size is used here:\r\n`test_loader = torch.utils.data.DataLoader(\r\n    datasets.MNIST('../data', train=False, transform=transforms.Compose([\r\n                       transforms.ToTensor(),\r\n                       transforms.Normalize((0.1307,), (0.3081,))\r\n                   ])),\r\n    batch_size=args.batch_size, shuffle=True, **kwargs)`\r\n\r\nAlso, what does this line(line 105) do:\r\n`test_loss = test_loss`\r\n\r\nand it seems that `epoch` is not used in test().\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.optim as optim\nfrom torchvision import datasets, transforms\nfrom torch.optim.lr_scheduler import StepLR\n\n\nclass Net(nn.Module):\n    def __init__(self):\n        super(Net, self).__init__()\n        self.conv1 = nn.Conv2d(1, 32, 3, 1)\n        self.conv2 = nn.Conv2d(32, 64, 3, 1)\n        self.dropout1 = nn.Dropout2d(0.25)\n        self.dropout2 = nn.Dropout2d(0.5)\n        self.fc1 = nn.Linear(9216, 128)\n        self.fc2 = nn.Linear(128, 10)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = F.relu(x)\n        x = self.conv2(x)\n        x = F.relu(x)\n        x = F.max_pool2d(x, 2)\n        x = self.dropout1(x)\n        x = torch.flatten(x, 1)\n        x = self.fc1(x)\n        x = F.relu(x)\n        x = self.dropout2(x)\n        x = self.fc2(x)\n        output = F.log_softmax(x, dim=1)\n        return output\n\n\ndef train(args, model, device, train_loader, optimizer, epoch):\n    model.train()\n    for batch_idx, (data, target) in enumerate(train_loader):\n        data, target = data.to(device), target.to(device)\n        optimizer.zero_grad()\n        output = model(data)\n        loss = F.nll_loss(output, target)\n        loss.backward()\n        optimizer.step()\n        if batch_idx % args.log_interval == 0:\n            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n                epoch, batch_idx * len(data), len(train_loader.dataset),\n                100. * batch_idx / len(train_loader), loss.item()))\n            if args.dry_run:\n                break\n\n\ndef test(model, device, test_loader):\n    model.eval()\n    test_loss = 0\n    correct = 0\n    with torch.no_grad():\n        for data, target in test_loader:\n            data, target = data.to(device), target.to(device)\n            output = model(data)\n            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss\n            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\n            correct += pred.eq(target.view_as(pred)).sum().item()\n\n    test_loss /= len(test_loader.dataset)\n\n    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n        test_loss, correct, len(test_loader.dataset),\n        100. * correct / len(test_loader.dataset)))\n\n\ndef main():\n    # Training settings\n    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n    parser.add_argument('--batch-size', type=int, default=64, metavar='N',\n                        help='input batch size for training (default: 64)')\n    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',\n                        help='input batch size for testing (default: 1000)')\n    parser.add_argument('--epochs', type=int, default=14, metavar='N',\n                        help='number of epochs to train (default: 14)')\n    parser.add_argument('--lr', type=float, default=1.0, metavar='LR',\n                        help='learning rate (default: 1.0)')\n    parser.add_argument('--gamma', type=float, default=0.7, metavar='M',\n                        help='Learning rate step gamma (default: 0.7)')\n    parser.add_argument('--no-cuda', action='store_true', default=False,\n                        help='disables CUDA training')\n    parser.add_argument('--dry-run', action='store_true', default=False,\n                        help='quickly check a single pass')\n    parser.add_argument('--seed', type=int, default=1, metavar='S',\n                        help='random seed (default: 1)')\n    parser.add_argument('--log-interval', type=int, default=10, metavar='N',\n                        help='how many batches to wait before logging training status')\n    parser.add_argument('--save-model', action='store_true', default=False,\n                        help='For Saving the current Model')\n    args = parser.parse_args()\n    use_cuda = not args.no_cuda and torch.cuda.is_available()\n\n    torch.manual_seed(args.seed)\n\n    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n\n    kwargs = {'batch_size': args.batch_size}\n    if use_cuda:\n        kwargs.update({'num_workers': 1,\n                       'pin_memory': True,\n                       'shuffle': True},\n                     )\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    train_loader = torch.utils.data.DataLoader(dataset1,**kwargs)\n    test_loader = torch.utils.data.DataLoader(dataset2, **kwargs)\n\n    model = Net().to(device)\n    optimizer = optim.Adadelta(model.parameters(), lr=args.lr)\n\n    scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)\n    for epoch in range(1, args.epochs + 1):\n        train(args, model, device, train_loader, optimizer, epoch)\n        test(model, device, test_loader)\n        scheduler.step()\n\n    if args.save_model:\n        torch.save(model.state_dict(), \"mnist_cnn.pt\")\n\n\nif __name__ == '__main__':\n    main()\n", "path": "mnist/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.optim as optim\nfrom torchvision import datasets, transforms\nfrom torch.optim.lr_scheduler import StepLR\n\n\nclass Net(nn.Module):\n    def __init__(self):\n        super(Net, self).__init__()\n        self.conv1 = nn.Conv2d(1, 32, 3, 1)\n        self.conv2 = nn.Conv2d(32, 64, 3, 1)\n        self.dropout1 = nn.Dropout2d(0.25)\n        self.dropout2 = nn.Dropout2d(0.5)\n        self.fc1 = nn.Linear(9216, 128)\n        self.fc2 = nn.Linear(128, 10)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = F.relu(x)\n        x = self.conv2(x)\n        x = F.relu(x)\n        x = F.max_pool2d(x, 2)\n        x = self.dropout1(x)\n        x = torch.flatten(x, 1)\n        x = self.fc1(x)\n        x = F.relu(x)\n        x = self.dropout2(x)\n        x = self.fc2(x)\n        output = F.log_softmax(x, dim=1)\n        return output\n\n\ndef train(args, model, device, train_loader, optimizer, epoch):\n    model.train()\n    for batch_idx, (data, target) in enumerate(train_loader):\n        data, target = data.to(device), target.to(device)\n        optimizer.zero_grad()\n        output = model(data)\n        loss = F.nll_loss(output, target)\n        loss.backward()\n        optimizer.step()\n        if batch_idx % args.log_interval == 0:\n            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n                epoch, batch_idx * len(data), len(train_loader.dataset),\n                100. * batch_idx / len(train_loader), loss.item()))\n            if args.dry_run:\n                break\n\n\ndef test(model, device, test_loader):\n    model.eval()\n    test_loss = 0\n    correct = 0\n    with torch.no_grad():\n        for data, target in test_loader:\n            data, target = data.to(device), target.to(device)\n            output = model(data)\n            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss\n            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\n            correct += pred.eq(target.view_as(pred)).sum().item()\n\n    test_loss /= len(test_loader.dataset)\n\n    print('\\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\\n'.format(\n        test_loss, correct, len(test_loader.dataset),\n        100. * correct / len(test_loader.dataset)))\n\n\ndef main():\n    # Training settings\n    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')\n    parser.add_argument('--batch-size', type=int, default=64, metavar='N',\n                        help='input batch size for training (default: 64)')\n    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',\n                        help='input batch size for testing (default: 1000)')\n    parser.add_argument('--epochs', type=int, default=14, metavar='N',\n                        help='number of epochs to train (default: 14)')\n    parser.add_argument('--lr', type=float, default=1.0, metavar='LR',\n                        help='learning rate (default: 1.0)')\n    parser.add_argument('--gamma', type=float, default=0.7, metavar='M',\n                        help='Learning rate step gamma (default: 0.7)')\n    parser.add_argument('--no-cuda', action='store_true', default=False,\n                        help='disables CUDA training')\n    parser.add_argument('--dry-run', action='store_true', default=False,\n                        help='quickly check a single pass')\n    parser.add_argument('--seed', type=int, default=1, metavar='S',\n                        help='random seed (default: 1)')\n    parser.add_argument('--log-interval', type=int, default=10, metavar='N',\n                        help='how many batches to wait before logging training status')\n    parser.add_argument('--save-model', action='store_true', default=False,\n                        help='For Saving the current Model')\n    args = parser.parse_args()\n    use_cuda = not args.no_cuda and torch.cuda.is_available()\n\n    torch.manual_seed(args.seed)\n\n    device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n\n    train_kwargs = {'batch_size': args.batch_size}\n    test_kwargs = {'batch_size': args.test_batch_size}\n    if use_cuda:\n        cuda_kwargs = {'num_workers': 1,\n                       'pin_memory': True,\n                       'shuffle': True}\n        train_kwargs.update(cuda_kwargs)\n        test_kwargs.update(cuda_kwargs)\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    train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs)\n    test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)\n\n    model = Net().to(device)\n    optimizer = optim.Adadelta(model.parameters(), lr=args.lr)\n\n    scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)\n    for epoch in range(1, args.epochs + 1):\n        train(args, model, device, train_loader, optimizer, epoch)\n        test(model, device, test_loader)\n        scheduler.step()\n\n    if args.save_model:\n        torch.save(model.state_dict(), \"mnist_cnn.pt\")\n\n\nif __name__ == '__main__':\n    main()\n", "path": "mnist/main.py"}]}