We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
TestConvolution2D.test_(forward, backward)_gpu_im2col does not check use_cudnn=False case
`TestConvolution2D.test_forward_gpu_im2col` and `TestConvolution2D.test_backward_gpu_im2col` are expected to test `Convolution2DFunction.backward_gpu` works correctly when CuDNN is disabled.

To achieve this, these test fixtures set `self.use_cudnn` attribute of the instance of `Convolution2D` to `False` . But what is actually passed to `convoluton_2d` function as `use_cudnn` option is the `use_cudnn` argument of `__init__` , not the attribute `self.use_cudnn` (See [here](https://github.com/pfnet/chainer/blob/af1f11d4e50b322286a041c416eddd4e0ee63d30/chainer/links/connection/convolution_2d.py#L75)).


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `chainer/links/connection/convolution_2d.py`
Content:
```
1 import numpy
2 
3 from chainer.functions.connection import convolution_2d
4 from chainer import link
5 
6 
7 class Convolution2D(link.Link):
8 
9     """Two-dimensional convolutional layer.
10 
11     This link wraps the :func:`~chainer.functions.convolution_2d` function and
12     holds the filter weight and bias vector as parameters.
13 
14     Args:
15         in_channels (int): Number of channels of input arrays.
16         out_channels (int): Number of channels of output arrays.
17         ksize (int or (int, int)): Size of filters (a.k.a. kernels).
18             ``ksize=k`` and ``ksize=(k, k)`` are equivalent.
19         stride (int or (int, int)): Stride of filter applications.
20             ``stride=s`` and ``stride=(s, s)`` are equivalent.
21         pad (int or (int, int)): Spatial padding width for input arrays.
22             ``pad=p`` and ``pad=(p, p)`` are equivalent.
23         wscale (float): Scaling factor of the initial weight.
24         bias (float): Initial bias value.
25         nobias (bool): If True, then this link does not use the bias term.
26         use_cudnn (bool): If True, then this link uses CuDNN if available.
27         initialW (4-D array): Initial weight value. If ``None``, then this
28             function uses to initialize ``wscale``.
29         initial_bias (1-D array): Initial bias value. If ``None``, then this
30             function uses to initialize ``bias``.
31 
32     .. seealso::
33        See :func:`chainer.functions.convolution_2d` for the definition of
34        two-dimensional convolution.
35 
36     Attributes:
37         W (~chainer.Variable): Weight parameter.
38         b (~chainer.Variable): Bias parameter.
39 
40     """
41     def __init__(self, in_channels, out_channels, ksize, stride=1, pad=0,
42                  wscale=1, bias=0, nobias=False, use_cudnn=True,
43                  initialW=None, initial_bias=None):
44         kh, kw = _pair(ksize)
45         self._conv_arg = (stride, pad, use_cudnn)
46 
47         W_shape = (out_channels, in_channels, kh, kw)
48         super(Convolution2D, self).__init__(W=W_shape)
49 
50         if initialW is not None:
51             self.W.data[...] = initialW
52         else:
53             std = wscale * numpy.sqrt(1. / (kh * kw * in_channels))
54             self.W.data[...] = numpy.random.normal(0, std, W_shape)
55 
56         if nobias:
57             self.b = None
58         else:
59             self.add_param('b', out_channels)
60             if initial_bias is None:
61                 initial_bias = bias
62             self.b.data[...] = initial_bias
63 
64     def __call__(self, x):
65         """Applies the convolution layer.
66 
67         Args:
68             x (~chainer.Variable): Input image.
69 
70         Returns:
71             ~chainer.Variable: Output of the convolution.
72 
73         """
74         return convolution_2d.convolution_2d(
75             x, self.W, self.b, *self._conv_arg)
76 
77 
78 def _pair(x):
79     if hasattr(x, '__getitem__'):
80         return x
81     return (x, x)
82
```
--- 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/chainer/links/connection/convolution_2d.py b/chainer/links/connection/convolution_2d.py
--- a/chainer/links/connection/convolution_2d.py
+++ b/chainer/links/connection/convolution_2d.py
@@ -42,7 +42,9 @@
                  wscale=1, bias=0, nobias=False, use_cudnn=True,
                  initialW=None, initial_bias=None):
         kh, kw = _pair(ksize)
-        self._conv_arg = (stride, pad, use_cudnn)
+        self.stride = _pair(stride)
+        self.pad = _pair(pad)
+        self.use_cudnn = use_cudnn
 
         W_shape = (out_channels, in_channels, kh, kw)
         super(Convolution2D, self).__init__(W=W_shape)
@@ -72,7 +74,7 @@
 
         """
         return convolution_2d.convolution_2d(
-            x, self.W, self.b, *self._conv_arg)
+            x, self.W, self.b, self.stride, self.pad, self.use_cudnn)
 
 
 def _pair(x):

{"golden_diff": "diff --git a/chainer/links/connection/convolution_2d.py b/chainer/links/connection/convolution_2d.py\n--- a/chainer/links/connection/convolution_2d.py\n+++ b/chainer/links/connection/convolution_2d.py\n@@ -42,7 +42,9 @@\n                  wscale=1, bias=0, nobias=False, use_cudnn=True,\n                  initialW=None, initial_bias=None):\n         kh, kw = _pair(ksize)\n-        self._conv_arg = (stride, pad, use_cudnn)\n+        self.stride = _pair(stride)\n+        self.pad = _pair(pad)\n+        self.use_cudnn = use_cudnn\n \n         W_shape = (out_channels, in_channels, kh, kw)\n         super(Convolution2D, self).__init__(W=W_shape)\n@@ -72,7 +74,7 @@\n \n         \"\"\"\n         return convolution_2d.convolution_2d(\n-            x, self.W, self.b, *self._conv_arg)\n+            x, self.W, self.b, self.stride, self.pad, self.use_cudnn)\n \n \n def _pair(x):\n", "issue": "TestConvolution2D.test_(forward, backward)_gpu_im2col does not check use_cudnn=False case\n`TestConvolution2D.test_forward_gpu_im2col` and `TestConvolution2D.test_backward_gpu_im2col` are expected to test `Convolution2DFunction.backward_gpu` works correctly when CuDNN is disabled.\n\nTo achieve this, these test fixtures set `self.use_cudnn` attribute of the instance of `Convolution2D` to `False` . But what is actually passed to `convoluton_2d` function as `use_cudnn` option is the `use_cudnn` argument of `__init__` , not the attribute `self.use_cudnn` (See [here](https://github.com/pfnet/chainer/blob/af1f11d4e50b322286a041c416eddd4e0ee63d30/chainer/links/connection/convolution_2d.py#L75)).\n\n", "before_files": [{"content": "import numpy\n\nfrom chainer.functions.connection import convolution_2d\nfrom chainer import link\n\n\nclass Convolution2D(link.Link):\n\n    \"\"\"Two-dimensional convolutional layer.\n\n    This link wraps the :func:`~chainer.functions.convolution_2d` function and\n    holds the filter weight and bias vector as parameters.\n\n    Args:\n        in_channels (int): Number of channels of input arrays.\n        out_channels (int): Number of channels of output arrays.\n        ksize (int or (int, int)): Size of filters (a.k.a. kernels).\n            ``ksize=k`` and ``ksize=(k, k)`` are equivalent.\n        stride (int or (int, int)): Stride of filter applications.\n            ``stride=s`` and ``stride=(s, s)`` are equivalent.\n        pad (int or (int, int)): Spatial padding width for input arrays.\n            ``pad=p`` and ``pad=(p, p)`` are equivalent.\n        wscale (float): Scaling factor of the initial weight.\n        bias (float): Initial bias value.\n        nobias (bool): If True, then this link does not use the bias term.\n        use_cudnn (bool): If True, then this link uses CuDNN if available.\n        initialW (4-D array): Initial weight value. If ``None``, then this\n            function uses to initialize ``wscale``.\n        initial_bias (1-D array): Initial bias value. If ``None``, then this\n            function uses to initialize ``bias``.\n\n    .. seealso::\n       See :func:`chainer.functions.convolution_2d` for the definition of\n       two-dimensional convolution.\n\n    Attributes:\n        W (~chainer.Variable): Weight parameter.\n        b (~chainer.Variable): Bias parameter.\n\n    \"\"\"\n    def __init__(self, in_channels, out_channels, ksize, stride=1, pad=0,\n                 wscale=1, bias=0, nobias=False, use_cudnn=True,\n                 initialW=None, initial_bias=None):\n        kh, kw = _pair(ksize)\n        self._conv_arg = (stride, pad, use_cudnn)\n\n        W_shape = (out_channels, in_channels, kh, kw)\n        super(Convolution2D, self).__init__(W=W_shape)\n\n        if initialW is not None:\n            self.W.data[...] = initialW\n        else:\n            std = wscale * numpy.sqrt(1. / (kh * kw * in_channels))\n            self.W.data[...] = numpy.random.normal(0, std, W_shape)\n\n        if nobias:\n            self.b = None\n        else:\n            self.add_param('b', out_channels)\n            if initial_bias is None:\n                initial_bias = bias\n            self.b.data[...] = initial_bias\n\n    def __call__(self, x):\n        \"\"\"Applies the convolution layer.\n\n        Args:\n            x (~chainer.Variable): Input image.\n\n        Returns:\n            ~chainer.Variable: Output of the convolution.\n\n        \"\"\"\n        return convolution_2d.convolution_2d(\n            x, self.W, self.b, *self._conv_arg)\n\n\ndef _pair(x):\n    if hasattr(x, '__getitem__'):\n        return x\n    return (x, x)\n", "path": "chainer/links/connection/convolution_2d.py"}], "after_files": [{"content": "import numpy\n\nfrom chainer.functions.connection import convolution_2d\nfrom chainer import link\n\n\nclass Convolution2D(link.Link):\n\n    \"\"\"Two-dimensional convolutional layer.\n\n    This link wraps the :func:`~chainer.functions.convolution_2d` function and\n    holds the filter weight and bias vector as parameters.\n\n    Args:\n        in_channels (int): Number of channels of input arrays.\n        out_channels (int): Number of channels of output arrays.\n        ksize (int or (int, int)): Size of filters (a.k.a. kernels).\n            ``ksize=k`` and ``ksize=(k, k)`` are equivalent.\n        stride (int or (int, int)): Stride of filter applications.\n            ``stride=s`` and ``stride=(s, s)`` are equivalent.\n        pad (int or (int, int)): Spatial padding width for input arrays.\n            ``pad=p`` and ``pad=(p, p)`` are equivalent.\n        wscale (float): Scaling factor of the initial weight.\n        bias (float): Initial bias value.\n        nobias (bool): If True, then this link does not use the bias term.\n        use_cudnn (bool): If True, then this link uses CuDNN if available.\n        initialW (4-D array): Initial weight value. If ``None``, then this\n            function uses to initialize ``wscale``.\n        initial_bias (1-D array): Initial bias value. If ``None``, then this\n            function uses to initialize ``bias``.\n\n    .. seealso::\n       See :func:`chainer.functions.convolution_2d` for the definition of\n       two-dimensional convolution.\n\n    Attributes:\n        W (~chainer.Variable): Weight parameter.\n        b (~chainer.Variable): Bias parameter.\n\n    \"\"\"\n    def __init__(self, in_channels, out_channels, ksize, stride=1, pad=0,\n                 wscale=1, bias=0, nobias=False, use_cudnn=True,\n                 initialW=None, initial_bias=None):\n        kh, kw = _pair(ksize)\n        self.stride = _pair(stride)\n        self.pad = _pair(pad)\n        self.use_cudnn = use_cudnn\n\n        W_shape = (out_channels, in_channels, kh, kw)\n        super(Convolution2D, self).__init__(W=W_shape)\n\n        if initialW is not None:\n            self.W.data[...] = initialW\n        else:\n            std = wscale * numpy.sqrt(1. / (kh * kw * in_channels))\n            self.W.data[...] = numpy.random.normal(0, std, W_shape)\n\n        if nobias:\n            self.b = None\n        else:\n            self.add_param('b', out_channels)\n            if initial_bias is None:\n                initial_bias = bias\n            self.b.data[...] = initial_bias\n\n    def __call__(self, x):\n        \"\"\"Applies the convolution layer.\n\n        Args:\n            x (~chainer.Variable): Input image.\n\n        Returns:\n            ~chainer.Variable: Output of the convolution.\n\n        \"\"\"\n        return convolution_2d.convolution_2d(\n            x, self.W, self.b, self.stride, self.pad, self.use_cudnn)\n\n\ndef _pair(x):\n    if hasattr(x, '__getitem__'):\n        return x\n    return (x, x)\n", "path": "chainer/links/connection/convolution_2d.py"}]}