We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Division error
Training a model for `fast-neural-style` raises a RuntimeError from variable division during input normalization. 

- python2.7
- torch==0.1.12.post2
- torchvision==0.1.8

````
Traceback (most recent call last):
  File "neural_style/neural_style.py", line 226, in <module>
    main()
  File "neural_style/neural_style.py", line 220, in main
    train(args)
  File "neural_style/neural_style.py", line 65, in train
    style_v = utils.normalize_batch(style_v)
  File "/home/paperspace/embro/neural_style/utils.py", line 42, in normalize_batch
    batch /= Variable(std)
  File "/usr/local/lib/python2.7/dist-packages/torch/autograd/variable.py", line 793, in __idiv__
    return self.div_(other)
  File "/usr/local/lib/python2.7/dist-packages/torch/autograd/variable.py", line 323, in div_
    raise RuntimeError("div_ only supports scalar multiplication")
````




--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `fast_neural_style/neural_style/utils.py`
Content:
```
1 import torch
2 from PIL import Image
3 from torch.autograd import Variable
4 
5 
6 def load_image(filename, size=None, scale=None):
7     img = Image.open(filename)
8     if size is not None:
9         img = img.resize((size, size), Image.ANTIALIAS)
10     elif scale is not None:
11         img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS)
12     return img
13 
14 
15 def save_image(filename, data):
16     img = data.clone().clamp(0, 255).numpy()
17     img = img.transpose(1, 2, 0).astype("uint8")
18     img = Image.fromarray(img)
19     img.save(filename)
20 
21 
22 def gram_matrix(y):
23     (b, ch, h, w) = y.size()
24     features = y.view(b, ch, w * h)
25     features_t = features.transpose(1, 2)
26     gram = features.bmm(features_t) / (ch * h * w)
27     return gram
28 
29 
30 def normalize_batch(batch):
31     # normalize using imagenet mean and std
32     mean = batch.data.new(batch.data.size())
33     std = batch.data.new(batch.data.size())
34     mean[:, 0, :, :] = 0.485
35     mean[:, 1, :, :] = 0.456
36     mean[:, 2, :, :] = 0.406
37     std[:, 0, :, :] = 0.229
38     std[:, 1, :, :] = 0.224
39     std[:, 2, :, :] = 0.225
40     batch = torch.div(batch, 255.0)
41     batch -= Variable(mean)
42     batch /= Variable(std)
43     return batch
44
```
--- 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/fast_neural_style/neural_style/utils.py b/fast_neural_style/neural_style/utils.py
--- a/fast_neural_style/neural_style/utils.py
+++ b/fast_neural_style/neural_style/utils.py
@@ -39,5 +39,5 @@
     std[:, 2, :, :] = 0.225
     batch = torch.div(batch, 255.0)
     batch -= Variable(mean)
-    batch /= Variable(std)
+    batch = batch / Variable(std)
     return batch

{"golden_diff": "diff --git a/fast_neural_style/neural_style/utils.py b/fast_neural_style/neural_style/utils.py\n--- a/fast_neural_style/neural_style/utils.py\n+++ b/fast_neural_style/neural_style/utils.py\n@@ -39,5 +39,5 @@\n     std[:, 2, :, :] = 0.225\n     batch = torch.div(batch, 255.0)\n     batch -= Variable(mean)\n-    batch /= Variable(std)\n+    batch = batch / Variable(std)\n     return batch\n", "issue": "Division error\nTraining a model for `fast-neural-style` raises a RuntimeError from variable division during input normalization. \r\n\r\n- python2.7\r\n- torch==0.1.12.post2\r\n- torchvision==0.1.8\r\n\r\n````\r\nTraceback (most recent call last):\r\n  File \"neural_style/neural_style.py\", line 226, in <module>\r\n    main()\r\n  File \"neural_style/neural_style.py\", line 220, in main\r\n    train(args)\r\n  File \"neural_style/neural_style.py\", line 65, in train\r\n    style_v = utils.normalize_batch(style_v)\r\n  File \"/home/paperspace/embro/neural_style/utils.py\", line 42, in normalize_batch\r\n    batch /= Variable(std)\r\n  File \"/usr/local/lib/python2.7/dist-packages/torch/autograd/variable.py\", line 793, in __idiv__\r\n    return self.div_(other)\r\n  File \"/usr/local/lib/python2.7/dist-packages/torch/autograd/variable.py\", line 323, in div_\r\n    raise RuntimeError(\"div_ only supports scalar multiplication\")\r\n````\r\n\r\n\r\n\n", "before_files": [{"content": "import torch\nfrom PIL import Image\nfrom torch.autograd import Variable\n\n\ndef load_image(filename, size=None, scale=None):\n    img = Image.open(filename)\n    if size is not None:\n        img = img.resize((size, size), Image.ANTIALIAS)\n    elif scale is not None:\n        img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS)\n    return img\n\n\ndef save_image(filename, data):\n    img = data.clone().clamp(0, 255).numpy()\n    img = img.transpose(1, 2, 0).astype(\"uint8\")\n    img = Image.fromarray(img)\n    img.save(filename)\n\n\ndef gram_matrix(y):\n    (b, ch, h, w) = y.size()\n    features = y.view(b, ch, w * h)\n    features_t = features.transpose(1, 2)\n    gram = features.bmm(features_t) / (ch * h * w)\n    return gram\n\n\ndef normalize_batch(batch):\n    # normalize using imagenet mean and std\n    mean = batch.data.new(batch.data.size())\n    std = batch.data.new(batch.data.size())\n    mean[:, 0, :, :] = 0.485\n    mean[:, 1, :, :] = 0.456\n    mean[:, 2, :, :] = 0.406\n    std[:, 0, :, :] = 0.229\n    std[:, 1, :, :] = 0.224\n    std[:, 2, :, :] = 0.225\n    batch = torch.div(batch, 255.0)\n    batch -= Variable(mean)\n    batch /= Variable(std)\n    return batch\n", "path": "fast_neural_style/neural_style/utils.py"}], "after_files": [{"content": "import torch\nfrom PIL import Image\nfrom torch.autograd import Variable\n\n\ndef load_image(filename, size=None, scale=None):\n    img = Image.open(filename)\n    if size is not None:\n        img = img.resize((size, size), Image.ANTIALIAS)\n    elif scale is not None:\n        img = img.resize((int(img.size[0] / scale), int(img.size[1] / scale)), Image.ANTIALIAS)\n    return img\n\n\ndef save_image(filename, data):\n    img = data.clone().clamp(0, 255).numpy()\n    img = img.transpose(1, 2, 0).astype(\"uint8\")\n    img = Image.fromarray(img)\n    img.save(filename)\n\n\ndef gram_matrix(y):\n    (b, ch, h, w) = y.size()\n    features = y.view(b, ch, w * h)\n    features_t = features.transpose(1, 2)\n    gram = features.bmm(features_t) / (ch * h * w)\n    return gram\n\n\ndef normalize_batch(batch):\n    # normalize using imagenet mean and std\n    mean = batch.data.new(batch.data.size())\n    std = batch.data.new(batch.data.size())\n    mean[:, 0, :, :] = 0.485\n    mean[:, 1, :, :] = 0.456\n    mean[:, 2, :, :] = 0.406\n    std[:, 0, :, :] = 0.229\n    std[:, 1, :, :] = 0.224\n    std[:, 2, :, :] = 0.225\n    batch = torch.div(batch, 255.0)\n    batch -= Variable(mean)\n    batch = batch / Variable(std)\n    return batch\n", "path": "fast_neural_style/neural_style/utils.py"}]}