prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  improve code quality using Codacy
  <!---
  **If you are reporting a bug:**
  *  Verify that your issue is not being currently addressed by other issues or pull requests.
  *  Please note that Auto-Keras is only compatible with **Python 3.6**.
  *  Tag the issue with the `bug report` tag.
  -->
  
  ### Bug Description
  <!---
  A clear and concise description of what the bug is.
  -->
  We are now using Codacy and CodeClimate to improve our code quality.
  
  Please try to solve anything reported by Codacy by following this [link](https://app.codacy.com/manual/jhfjhfj1/autokeras/issues/index).
  On the page, you can see a list of **issues**.
  You can click to see the details and suggestions to fix the issue.
  
  Thanks.
  
  </issue>
  <code>
  [start of autokeras/pretrained/voice_generator/deepvoice3_pytorch/conv.py]
  1 # coding: utf-8
  2 from torch import nn
  3 from torch.nn import functional as F
  4 
  5 
  6 class Conv1d(nn.Conv1d):
  7     """Extended nn.Conv1d for incremental dilated convolutions
  8     """
  9 
  10     def __init__(self, *args, **kwargs):
  11         super().__init__(*args, **kwargs)
  12         self.clear_buffer()
  13         self._linearized_weight = None
  14 
  15     def incremental_forward(self, input):
  16 
  17         # reshape weight
  18         weight = self._get_linearized_weight()
  19         kw = self.kernel_size[0]
  20         dilation = self.dilation[0]
  21 
  22         bsz = input.size(0)  # input: bsz x len x dim
  23         if kw > 1:
  24             input = input.data
  25             if self.input_buffer is None:
  26                 self.input_buffer = input.new(bsz, kw + (kw - 1) * (dilation - 1), input.size(2))
  27                 self.input_buffer.zero_()
  28             else:
  29                 # shift buffer
  30                 self.input_buffer[:, :-1, :] = self.input_buffer[:, 1:, :].clone()
  31             # append next input
  32             self.input_buffer[:, -1, :] = input[:, -1, :]
  33             input = self.input_buffer
  34             if dilation > 1:
  35                 input = input[:, 0::dilation, :].contiguous()
  36         output = F.linear(input.view(bsz, -1), weight, self.bias)
  37         return output.view(bsz, 1, -1)
  38 
  39     def clear_buffer(self):
  40         self.input_buffer = None
  41 
  42     def _get_linearized_weight(self):
  43         if self._linearized_weight is None:
  44             kw = self.kernel_size[0]
  45             # nn.Conv1d
  46             weight = self.weight.transpose(1, 2).contiguous()
  47 
  48             assert weight.size() == (self.out_channels, kw, self.in_channels)
  49             self._linearized_weight = weight.view(self.out_channels, -1)
  50         return self._linearized_weight
  51
  [end of autokeras/pretrained/voice_generator/deepvoice3_pytorch/conv.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/autokeras/pretrained/voice_generator/deepvoice3_pytorch/conv.py b/autokeras/pretrained/voice_generator/deepvoice3_pytorch/conv.py
  --- a/autokeras/pretrained/voice_generator/deepvoice3_pytorch/conv.py
  +++ b/autokeras/pretrained/voice_generator/deepvoice3_pytorch/conv.py
  @@ -12,29 +12,29 @@
           self.clear_buffer()
           self._linearized_weight = None
   
  -    def incremental_forward(self, input):
  +    def incremental_forward(self, input_data):
   
           # reshape weight
           weight = self._get_linearized_weight()
           kw = self.kernel_size[0]
           dilation = self.dilation[0]
   
  -        bsz = input.size(0)  # input: bsz x len x dim
  +        bsz = input_data.size(0)  # conv_input: bsz x len x dim
           if kw > 1:
  -            input = input.data
  +            input_data = input_data.data
               if self.input_buffer is None:
  -                self.input_buffer = input.new(bsz, kw + (kw - 1) * (dilation - 1), input.size(2))
  +                self.input_buffer = input_data.new(bsz, kw + (kw - 1) * (dilation - 1), input_data.size(2))
                   self.input_buffer.zero_()
               else:
                   # shift buffer
                   self.input_buffer[:, :-1, :] = self.input_buffer[:, 1:, :].clone()
               # append next input
  -            self.input_buffer[:, -1, :] = input[:, -1, :]
  -            input = self.input_buffer
  +            self.input_buffer[:, -1, :] = input_data[:, -1, :]
  +            input_data = self.input_buffer
               if dilation > 1:
  -                input = input[:, 0::dilation, :].contiguous()
  -        output = F.linear(input.view(bsz, -1), weight, self.bias)
  -        return output.view(bsz, 1, -1)
  +                input_data = input_data[:, 0::dilation, :].contiguous()
  +        input_data = F.linear(input_data.view(bsz, -1), weight, self.bias)
  +        return input_data.view(bsz, 1, -1)
   
       def clear_buffer(self):
           self.input_buffer = None
  
verification_info: {"golden_diff": "diff --git a/autokeras/pretrained/voice_generator/deepvoice3_pytorch/conv.py b/autokeras/pretrained/voice_generator/deepvoice3_pytorch/conv.py\n--- a/autokeras/pretrained/voice_generator/deepvoice3_pytorch/conv.py\n+++ b/autokeras/pretrained/voice_generator/deepvoice3_pytorch/conv.py\n@@ -12,29 +12,29 @@\n         self.clear_buffer()\n         self._linearized_weight = None\n \n-    def incremental_forward(self, input):\n+    def incremental_forward(self, input_data):\n \n         # reshape weight\n         weight = self._get_linearized_weight()\n         kw = self.kernel_size[0]\n         dilation = self.dilation[0]\n \n-        bsz = input.size(0)  # input: bsz x len x dim\n+        bsz = input_data.size(0)  # conv_input: bsz x len x dim\n         if kw > 1:\n-            input = input.data\n+            input_data = input_data.data\n             if self.input_buffer is None:\n-                self.input_buffer = input.new(bsz, kw + (kw - 1) * (dilation - 1), input.size(2))\n+                self.input_buffer = input_data.new(bsz, kw + (kw - 1) * (dilation - 1), input_data.size(2))\n                 self.input_buffer.zero_()\n             else:\n                 # shift buffer\n                 self.input_buffer[:, :-1, :] = self.input_buffer[:, 1:, :].clone()\n             # append next input\n-            self.input_buffer[:, -1, :] = input[:, -1, :]\n-            input = self.input_buffer\n+            self.input_buffer[:, -1, :] = input_data[:, -1, :]\n+            input_data = self.input_buffer\n             if dilation > 1:\n-                input = input[:, 0::dilation, :].contiguous()\n-        output = F.linear(input.view(bsz, -1), weight, self.bias)\n-        return output.view(bsz, 1, -1)\n+                input_data = input_data[:, 0::dilation, :].contiguous()\n+        input_data = F.linear(input_data.view(bsz, -1), weight, self.bias)\n+        return input_data.view(bsz, 1, -1)\n \n     def clear_buffer(self):\n         self.input_buffer = None\n", "issue": "improve code quality using Codacy\n<!---\r\n**If you are reporting a bug:**\r\n*  Verify that your issue is not being currently addressed by other issues or pull requests.\r\n*  Please note that Auto-Keras is only compatible with **Python 3.6**.\r\n*  Tag the issue with the `bug report` tag.\r\n-->\r\n\r\n### Bug Description\r\n<!---\r\nA clear and concise description of what the bug is.\r\n-->\r\nWe are now using Codacy and CodeClimate to improve our code quality.\r\n\r\nPlease try to solve anything reported by Codacy by following this [link](https://app.codacy.com/manual/jhfjhfj1/autokeras/issues/index).\r\nOn the page, you can see a list of **issues**.\r\nYou can click to see the details and suggestions to fix the issue.\r\n\r\nThanks.\n", "before_files": [{"content": "# coding: utf-8\nfrom torch import nn\nfrom torch.nn import functional as F\n\n\nclass Conv1d(nn.Conv1d):\n    \"\"\"Extended nn.Conv1d for incremental dilated convolutions\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.clear_buffer()\n        self._linearized_weight = None\n\n    def incremental_forward(self, input):\n\n        # reshape weight\n        weight = self._get_linearized_weight()\n        kw = self.kernel_size[0]\n        dilation = self.dilation[0]\n\n        bsz = input.size(0)  # input: bsz x len x dim\n        if kw > 1:\n            input = input.data\n            if self.input_buffer is None:\n                self.input_buffer = input.new(bsz, kw + (kw - 1) * (dilation - 1), input.size(2))\n                self.input_buffer.zero_()\n            else:\n                # shift buffer\n                self.input_buffer[:, :-1, :] = self.input_buffer[:, 1:, :].clone()\n            # append next input\n            self.input_buffer[:, -1, :] = input[:, -1, :]\n            input = self.input_buffer\n            if dilation > 1:\n                input = input[:, 0::dilation, :].contiguous()\n        output = F.linear(input.view(bsz, -1), weight, self.bias)\n        return output.view(bsz, 1, -1)\n\n    def clear_buffer(self):\n        self.input_buffer = None\n\n    def _get_linearized_weight(self):\n        if self._linearized_weight is None:\n            kw = self.kernel_size[0]\n            # nn.Conv1d\n            weight = self.weight.transpose(1, 2).contiguous()\n\n            assert weight.size() == (self.out_channels, kw, self.in_channels)\n            self._linearized_weight = weight.view(self.out_channels, -1)\n        return self._linearized_weight\n", "path": "autokeras/pretrained/voice_generator/deepvoice3_pytorch/conv.py"}]}