prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  [super_resolution]
  def _get_orthogonal_init_weights(weights):
      fan_out = weights.size(0)
      fan_in = weights.size(1) * weights.size(2) * weights.size(3)
      u, _, v = svd(normal(0.0, 1.0, (fan_out, fan_in)), full_matrices=False)
      if u.shape == (fan_out, fan_in):
          return torch.Tensor(u.reshape(weights.size()))
      else:
          return torch.Tensor(v.reshape(weights.size()))
  
  Why do the above operation？
  
  </issue>
  <code>
  [start of super_resolution/model.py]
  1 import torch
  2 import torch.nn as nn
  3 from numpy.random import normal
  4 from numpy.linalg import svd
  5 from math import sqrt
  6 
  7 
  8 def _get_orthogonal_init_weights(weights):
  9     fan_out = weights.size(0)
  10     fan_in = weights.size(1) * weights.size(2) * weights.size(3)
  11 
  12     u, _, v = svd(normal(0.0, 1.0, (fan_out, fan_in)), full_matrices=False)
  13 
  14     if u.shape == (fan_out, fan_in):
  15         return torch.Tensor(u.reshape(weights.size()))
  16     else:
  17         return torch.Tensor(v.reshape(weights.size()))
  18 
  19 
  20 class Net(nn.Module):
  21     def __init__(self, upscale_factor):
  22         super(Net, self).__init__()
  23 
  24         self.relu = nn.ReLU()
  25         self.conv1 = nn.Conv2d(1, 64, (5, 5), (1, 1), (2, 2))
  26         self.conv2 = nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1))
  27         self.conv3 = nn.Conv2d(64, 32, (3, 3), (1, 1), (1, 1))
  28         self.conv4 = nn.Conv2d(32, upscale_factor ** 2, (3, 3), (1, 1), (1, 1))
  29         self.pixel_shuffle = nn.PixelShuffle(upscale_factor)
  30 
  31         self._initialize_weights()
  32 
  33     def forward(self, x):
  34         x = self.relu(self.conv1(x))
  35         x = self.relu(self.conv2(x))
  36         x = self.relu(self.conv3(x))
  37         x = self.pixel_shuffle(self.conv4(x))
  38         return x
  39 
  40     def _initialize_weights(self):
  41         self.conv1.weight.data.copy_(_get_orthogonal_init_weights(self.conv1.weight) * sqrt(2))
  42         self.conv2.weight.data.copy_(_get_orthogonal_init_weights(self.conv2.weight) * sqrt(2))
  43         self.conv3.weight.data.copy_(_get_orthogonal_init_weights(self.conv3.weight) * sqrt(2))
  44         self.conv4.weight.data.copy_(_get_orthogonal_init_weights(self.conv4.weight))
  45
  [end of super_resolution/model.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/super_resolution/model.py b/super_resolution/model.py
  --- a/super_resolution/model.py
  +++ b/super_resolution/model.py
  @@ -1,22 +1,11 @@
   import torch
   import torch.nn as nn
  +import torch.nn.init as init
   from numpy.random import normal
   from numpy.linalg import svd
   from math import sqrt
   
   
  -def _get_orthogonal_init_weights(weights):
  -    fan_out = weights.size(0)
  -    fan_in = weights.size(1) * weights.size(2) * weights.size(3)
  -
  -    u, _, v = svd(normal(0.0, 1.0, (fan_out, fan_in)), full_matrices=False)
  -
  -    if u.shape == (fan_out, fan_in):
  -        return torch.Tensor(u.reshape(weights.size()))
  -    else:
  -        return torch.Tensor(v.reshape(weights.size()))
  -
  -
   class Net(nn.Module):
       def __init__(self, upscale_factor):
           super(Net, self).__init__()
  @@ -38,7 +27,7 @@
           return x
   
       def _initialize_weights(self):
  -        self.conv1.weight.data.copy_(_get_orthogonal_init_weights(self.conv1.weight) * sqrt(2))
  -        self.conv2.weight.data.copy_(_get_orthogonal_init_weights(self.conv2.weight) * sqrt(2))
  -        self.conv3.weight.data.copy_(_get_orthogonal_init_weights(self.conv3.weight) * sqrt(2))
  -        self.conv4.weight.data.copy_(_get_orthogonal_init_weights(self.conv4.weight))
  +        init.orthogonal(self.conv1.weight, init.gain('relu'))
  +        init.orthogonal(self.conv2.weight, init.gain('relu'))
  +        init.orthogonal(self.conv3.weight, init.gain('relu'))
  +        init.orthogonal(self.conv4.weight)
  
verification_info: {"golden_diff": "diff --git a/super_resolution/model.py b/super_resolution/model.py\n--- a/super_resolution/model.py\n+++ b/super_resolution/model.py\n@@ -1,22 +1,11 @@\n import torch\n import torch.nn as nn\n+import torch.nn.init as init\n from numpy.random import normal\n from numpy.linalg import svd\n from math import sqrt\n \n \n-def _get_orthogonal_init_weights(weights):\n-    fan_out = weights.size(0)\n-    fan_in = weights.size(1) * weights.size(2) * weights.size(3)\n-\n-    u, _, v = svd(normal(0.0, 1.0, (fan_out, fan_in)), full_matrices=False)\n-\n-    if u.shape == (fan_out, fan_in):\n-        return torch.Tensor(u.reshape(weights.size()))\n-    else:\n-        return torch.Tensor(v.reshape(weights.size()))\n-\n-\n class Net(nn.Module):\n     def __init__(self, upscale_factor):\n         super(Net, self).__init__()\n@@ -38,7 +27,7 @@\n         return x\n \n     def _initialize_weights(self):\n-        self.conv1.weight.data.copy_(_get_orthogonal_init_weights(self.conv1.weight) * sqrt(2))\n-        self.conv2.weight.data.copy_(_get_orthogonal_init_weights(self.conv2.weight) * sqrt(2))\n-        self.conv3.weight.data.copy_(_get_orthogonal_init_weights(self.conv3.weight) * sqrt(2))\n-        self.conv4.weight.data.copy_(_get_orthogonal_init_weights(self.conv4.weight))\n+        init.orthogonal(self.conv1.weight, init.gain('relu'))\n+        init.orthogonal(self.conv2.weight, init.gain('relu'))\n+        init.orthogonal(self.conv3.weight, init.gain('relu'))\n+        init.orthogonal(self.conv4.weight)\n", "issue": "[super_resolution]\ndef _get_orthogonal_init_weights(weights):\r\n    fan_out = weights.size(0)\r\n    fan_in = weights.size(1) * weights.size(2) * weights.size(3)\r\n    u, _, v = svd(normal(0.0, 1.0, (fan_out, fan_in)), full_matrices=False)\r\n    if u.shape == (fan_out, fan_in):\r\n        return torch.Tensor(u.reshape(weights.size()))\r\n    else:\r\n        return torch.Tensor(v.reshape(weights.size()))\r\n\r\nWhy do the above operation\uff1f\n", "before_files": [{"content": "import torch\nimport torch.nn as nn\nfrom numpy.random import normal\nfrom numpy.linalg import svd\nfrom math import sqrt\n\n\ndef _get_orthogonal_init_weights(weights):\n    fan_out = weights.size(0)\n    fan_in = weights.size(1) * weights.size(2) * weights.size(3)\n\n    u, _, v = svd(normal(0.0, 1.0, (fan_out, fan_in)), full_matrices=False)\n\n    if u.shape == (fan_out, fan_in):\n        return torch.Tensor(u.reshape(weights.size()))\n    else:\n        return torch.Tensor(v.reshape(weights.size()))\n\n\nclass Net(nn.Module):\n    def __init__(self, upscale_factor):\n        super(Net, self).__init__()\n\n        self.relu = nn.ReLU()\n        self.conv1 = nn.Conv2d(1, 64, (5, 5), (1, 1), (2, 2))\n        self.conv2 = nn.Conv2d(64, 64, (3, 3), (1, 1), (1, 1))\n        self.conv3 = nn.Conv2d(64, 32, (3, 3), (1, 1), (1, 1))\n        self.conv4 = nn.Conv2d(32, upscale_factor ** 2, (3, 3), (1, 1), (1, 1))\n        self.pixel_shuffle = nn.PixelShuffle(upscale_factor)\n\n        self._initialize_weights()\n\n    def forward(self, x):\n        x = self.relu(self.conv1(x))\n        x = self.relu(self.conv2(x))\n        x = self.relu(self.conv3(x))\n        x = self.pixel_shuffle(self.conv4(x))\n        return x\n\n    def _initialize_weights(self):\n        self.conv1.weight.data.copy_(_get_orthogonal_init_weights(self.conv1.weight) * sqrt(2))\n        self.conv2.weight.data.copy_(_get_orthogonal_init_weights(self.conv2.weight) * sqrt(2))\n        self.conv3.weight.data.copy_(_get_orthogonal_init_weights(self.conv3.weight) * sqrt(2))\n        self.conv4.weight.data.copy_(_get_orthogonal_init_weights(self.conv4.weight))\n", "path": "super_resolution/model.py"}]}