We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN 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？

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `super_resolution/model.py`
Content:
```
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 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/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)

{"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"}], "after_files": [{"content": "import torch\nimport torch.nn as nn\nimport torch.nn.init as init\nfrom numpy.random import normal\nfrom numpy.linalg import svd\nfrom math import sqrt\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        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", "path": "super_resolution/model.py"}]}