prompt: 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()
  
  ```
  
golden_diff: 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
  
verification_info: {"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"}]}