prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <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")
  ````
  
  
  
  
  </issue>
  <code>
  [start of fast_neural_style/neural_style/utils.py]
  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 of fast_neural_style/neural_style/utils.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
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"}]}