prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Test N-dimensional convolution link for dtypes of FP16 and FP64
  Follows #1279 and #1556.
  
  Since #1295 is now merged to master, we can add test for dtypes of FP16 and FP64 to N-dimensional convolution **LINK**.
  
  
  --- 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_nd.py`
  Content:
  ```
  1 from chainer.functions.connection import convolution_nd
  2 from chainer import initializers
  3 from chainer import link
  4 from chainer.utils import conv_nd
  5 
  6 
  7 class ConvolutionND(link.Link):
  8     """N-dimensional convolution layer.
  9 
  10     This link wraps the :func:`~chainer.functions.convolution_nd` function and
  11     holds the filter weight and bias vector as parameters.
  12 
  13     Args:
  14         ndim (int): Number of spatial dimensions.
  15         in_channels (int): Number of channels of input arrays.
  16         out_channels (int): Number of channels of output arrays.
  17         ksize (int or tuple of ints): Size of filters (a.k.a. kernels).
  18             ``ksize=k`` and ``ksize=(k, k, ..., k)`` are equivalent.
  19         stride (int or tuple of ints): Stride of filter application.
  20             ``stride=s`` and ``stride=(s, s, ..., s)`` are equivalent.
  21         pad (int or tuple of ints): Spatial padding width for input arrays.
  22             ``pad=p`` and ``pad=(p, p, ..., p)`` are equivalent.
  23         initialW: Value used to initialize the filter weight. May be an
  24             initializer instance or another value that
  25             :func:`~chainer.init_weight` helper function can take. This link
  26             uses :func:`~chainer.init_weight` to initialize the filter weight
  27             and passes the value of ``initialW`` to it as it is.
  28         initial_bias: Value used to initialize the bias vector. May be an
  29             initializer instance or another value except ``None`` that
  30             :func:`~chainer.init_weight` helper function can take. If ``None``
  31             is given, this link does not use the bias vector. This link uses
  32             :func:`~chainer.init_weight` to initialize the bias vector and
  33             passes the value of ``initial_bias`` other than ``None`` to it as
  34             it is.
  35         use_cudnn (bool): If ``True``, then this link uses cuDNN if available.
  36             See :func:`~chainer.functions.convolution_nd` for exact conditions
  37             of cuDNN availability.
  38         cover_all (bool): If ``True``, all spatial locations are convoluted
  39             into some output pixels. It may make the output size larger.
  40             ``cover_all`` needs to be ``False`` if you want to use cuDNN.
  41 
  42     .. seealso::
  43         See :func:`~chainer.functions.convolution_nd` for the definition of
  44         N-dimensional convolution. See
  45         :func:`~chainer.functions.convolution_2d` for the definition of
  46         two-dimensional convolution.
  47 
  48     Attributes:
  49         W (~chainer.Variable): Weight parameter.
  50         b (~chainer.Variable): Bias parameter. If ``initial_bias`` is ``None``,
  51             set to ``None``.
  52 
  53     """
  54 
  55     def __init__(self, ndim, in_channels, out_channels, ksize, stride=1, pad=0,
  56                  initialW=None, initial_bias=None, use_cudnn=True,
  57                  cover_all=False):
  58         ksize = conv_nd.as_tuple(ksize, ndim)
  59         self.stride = stride
  60         self.pad = pad
  61         self.use_cudnn = use_cudnn
  62         self.cover_all = cover_all
  63 
  64         W_shape = (out_channels, in_channels) + ksize
  65         super(ConvolutionND, self).__init__(W=W_shape)
  66         initializers.init_weight(self.W.data, initialW)
  67 
  68         if initial_bias is None:
  69             self.b = None
  70         else:
  71             self.add_param('b', out_channels)
  72             initializers.init_weight(self.b.data, initial_bias)
  73 
  74     def __call__(self, x):
  75         """Applies N-dimensional convolution layer.
  76 
  77         Args:
  78             x (~chainer.Variable): Input image.
  79 
  80         Returns:
  81             ~chainer.Variable: Output of convolution.
  82 
  83         """
  84         return convolution_nd.convolution_nd(
  85             x, self.W, self.b, self.stride, self.pad,
  86             use_cudnn=self.use_cudnn, cover_all=self.cover_all)
  87
  ```
  --- 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()
  
  ```
  
golden_diff: diff --git a/chainer/links/connection/convolution_nd.py b/chainer/links/connection/convolution_nd.py
  --- a/chainer/links/connection/convolution_nd.py
  +++ b/chainer/links/connection/convolution_nd.py
  @@ -22,16 +22,11 @@
               ``pad=p`` and ``pad=(p, p, ..., p)`` are equivalent.
           initialW: Value used to initialize the filter weight. May be an
               initializer instance or another value that
  -            :func:`~chainer.init_weight` helper function can take. This link
  -            uses :func:`~chainer.init_weight` to initialize the filter weight
  -            and passes the value of ``initialW`` to it as it is.
  +            :func:`~chainer.init_weight` helper function can take.
           initial_bias: Value used to initialize the bias vector. May be an
               initializer instance or another value except ``None`` that
               :func:`~chainer.init_weight` helper function can take. If ``None``
  -            is given, this link does not use the bias vector. This link uses
  -            :func:`~chainer.init_weight` to initialize the bias vector and
  -            passes the value of ``initial_bias`` other than ``None`` to it as
  -            it is.
  +            is given, this link does not use the bias vector.
           use_cudnn (bool): If ``True``, then this link uses cuDNN if available.
               See :func:`~chainer.functions.convolution_nd` for exact conditions
               of cuDNN availability.
  @@ -61,15 +56,17 @@
           self.use_cudnn = use_cudnn
           self.cover_all = cover_all
   
  +        super(ConvolutionND, self).__init__()
  +
           W_shape = (out_channels, in_channels) + ksize
  -        super(ConvolutionND, self).__init__(W=W_shape)
  -        initializers.init_weight(self.W.data, initialW)
  +        initialW = initializers._get_initializer(initialW)
  +        self.add_param('W', W_shape, initializer=initialW)
   
           if initial_bias is None:
               self.b = None
           else:
  -            self.add_param('b', out_channels)
  -            initializers.init_weight(self.b.data, initial_bias)
  +            initial_bias = initializers._get_initializer(initial_bias)
  +            self.add_param('b', out_channels, initializer=initial_bias)
   
       def __call__(self, x):
           """Applies N-dimensional convolution layer.
  
verification_info: {"golden_diff": "diff --git a/chainer/links/connection/convolution_nd.py b/chainer/links/connection/convolution_nd.py\n--- a/chainer/links/connection/convolution_nd.py\n+++ b/chainer/links/connection/convolution_nd.py\n@@ -22,16 +22,11 @@\n             ``pad=p`` and ``pad=(p, p, ..., p)`` are equivalent.\n         initialW: Value used to initialize the filter weight. May be an\n             initializer instance or another value that\n-            :func:`~chainer.init_weight` helper function can take. This link\n-            uses :func:`~chainer.init_weight` to initialize the filter weight\n-            and passes the value of ``initialW`` to it as it is.\n+            :func:`~chainer.init_weight` helper function can take.\n         initial_bias: Value used to initialize the bias vector. May be an\n             initializer instance or another value except ``None`` that\n             :func:`~chainer.init_weight` helper function can take. If ``None``\n-            is given, this link does not use the bias vector. This link uses\n-            :func:`~chainer.init_weight` to initialize the bias vector and\n-            passes the value of ``initial_bias`` other than ``None`` to it as\n-            it is.\n+            is given, this link does not use the bias vector.\n         use_cudnn (bool): If ``True``, then this link uses cuDNN if available.\n             See :func:`~chainer.functions.convolution_nd` for exact conditions\n             of cuDNN availability.\n@@ -61,15 +56,17 @@\n         self.use_cudnn = use_cudnn\n         self.cover_all = cover_all\n \n+        super(ConvolutionND, self).__init__()\n+\n         W_shape = (out_channels, in_channels) + ksize\n-        super(ConvolutionND, self).__init__(W=W_shape)\n-        initializers.init_weight(self.W.data, initialW)\n+        initialW = initializers._get_initializer(initialW)\n+        self.add_param('W', W_shape, initializer=initialW)\n \n         if initial_bias is None:\n             self.b = None\n         else:\n-            self.add_param('b', out_channels)\n-            initializers.init_weight(self.b.data, initial_bias)\n+            initial_bias = initializers._get_initializer(initial_bias)\n+            self.add_param('b', out_channels, initializer=initial_bias)\n \n     def __call__(self, x):\n         \"\"\"Applies N-dimensional convolution layer.\n", "issue": "Test N-dimensional convolution link for dtypes of FP16 and FP64\nFollows #1279 and #1556.\n\nSince #1295 is now merged to master, we can add test for dtypes of FP16 and FP64 to N-dimensional convolution **LINK**.\n\n", "before_files": [{"content": "from chainer.functions.connection import convolution_nd\nfrom chainer import initializers\nfrom chainer import link\nfrom chainer.utils import conv_nd\n\n\nclass ConvolutionND(link.Link):\n    \"\"\"N-dimensional convolution layer.\n\n    This link wraps the :func:`~chainer.functions.convolution_nd` function and\n    holds the filter weight and bias vector as parameters.\n\n    Args:\n        ndim (int): Number of spatial dimensions.\n        in_channels (int): Number of channels of input arrays.\n        out_channels (int): Number of channels of output arrays.\n        ksize (int or tuple of ints): Size of filters (a.k.a. kernels).\n            ``ksize=k`` and ``ksize=(k, k, ..., k)`` are equivalent.\n        stride (int or tuple of ints): Stride of filter application.\n            ``stride=s`` and ``stride=(s, s, ..., s)`` are equivalent.\n        pad (int or tuple of ints): Spatial padding width for input arrays.\n            ``pad=p`` and ``pad=(p, p, ..., p)`` are equivalent.\n        initialW: Value used to initialize the filter weight. May be an\n            initializer instance or another value that\n            :func:`~chainer.init_weight` helper function can take. This link\n            uses :func:`~chainer.init_weight` to initialize the filter weight\n            and passes the value of ``initialW`` to it as it is.\n        initial_bias: Value used to initialize the bias vector. May be an\n            initializer instance or another value except ``None`` that\n            :func:`~chainer.init_weight` helper function can take. If ``None``\n            is given, this link does not use the bias vector. This link uses\n            :func:`~chainer.init_weight` to initialize the bias vector and\n            passes the value of ``initial_bias`` other than ``None`` to it as\n            it is.\n        use_cudnn (bool): If ``True``, then this link uses cuDNN if available.\n            See :func:`~chainer.functions.convolution_nd` for exact conditions\n            of cuDNN availability.\n        cover_all (bool): If ``True``, all spatial locations are convoluted\n            into some output pixels. It may make the output size larger.\n            ``cover_all`` needs to be ``False`` if you want to use cuDNN.\n\n    .. seealso::\n        See :func:`~chainer.functions.convolution_nd` for the definition of\n        N-dimensional convolution. See\n        :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. If ``initial_bias`` is ``None``,\n            set to ``None``.\n\n    \"\"\"\n\n    def __init__(self, ndim, in_channels, out_channels, ksize, stride=1, pad=0,\n                 initialW=None, initial_bias=None, use_cudnn=True,\n                 cover_all=False):\n        ksize = conv_nd.as_tuple(ksize, ndim)\n        self.stride = stride\n        self.pad = pad\n        self.use_cudnn = use_cudnn\n        self.cover_all = cover_all\n\n        W_shape = (out_channels, in_channels) + ksize\n        super(ConvolutionND, self).__init__(W=W_shape)\n        initializers.init_weight(self.W.data, initialW)\n\n        if initial_bias is None:\n            self.b = None\n        else:\n            self.add_param('b', out_channels)\n            initializers.init_weight(self.b.data, initial_bias)\n\n    def __call__(self, x):\n        \"\"\"Applies N-dimensional convolution layer.\n\n        Args:\n            x (~chainer.Variable): Input image.\n\n        Returns:\n            ~chainer.Variable: Output of convolution.\n\n        \"\"\"\n        return convolution_nd.convolution_nd(\n            x, self.W, self.b, self.stride, self.pad,\n            use_cudnn=self.use_cudnn, cover_all=self.cover_all)\n", "path": "chainer/links/connection/convolution_nd.py"}], "after_files": [{"content": "from chainer.functions.connection import convolution_nd\nfrom chainer import initializers\nfrom chainer import link\nfrom chainer.utils import conv_nd\n\n\nclass ConvolutionND(link.Link):\n    \"\"\"N-dimensional convolution layer.\n\n    This link wraps the :func:`~chainer.functions.convolution_nd` function and\n    holds the filter weight and bias vector as parameters.\n\n    Args:\n        ndim (int): Number of spatial dimensions.\n        in_channels (int): Number of channels of input arrays.\n        out_channels (int): Number of channels of output arrays.\n        ksize (int or tuple of ints): Size of filters (a.k.a. kernels).\n            ``ksize=k`` and ``ksize=(k, k, ..., k)`` are equivalent.\n        stride (int or tuple of ints): Stride of filter application.\n            ``stride=s`` and ``stride=(s, s, ..., s)`` are equivalent.\n        pad (int or tuple of ints): Spatial padding width for input arrays.\n            ``pad=p`` and ``pad=(p, p, ..., p)`` are equivalent.\n        initialW: Value used to initialize the filter weight. May be an\n            initializer instance or another value that\n            :func:`~chainer.init_weight` helper function can take.\n        initial_bias: Value used to initialize the bias vector. May be an\n            initializer instance or another value except ``None`` that\n            :func:`~chainer.init_weight` helper function can take. If ``None``\n            is given, this link does not use the bias vector.\n        use_cudnn (bool): If ``True``, then this link uses cuDNN if available.\n            See :func:`~chainer.functions.convolution_nd` for exact conditions\n            of cuDNN availability.\n        cover_all (bool): If ``True``, all spatial locations are convoluted\n            into some output pixels. It may make the output size larger.\n            ``cover_all`` needs to be ``False`` if you want to use cuDNN.\n\n    .. seealso::\n        See :func:`~chainer.functions.convolution_nd` for the definition of\n        N-dimensional convolution. See\n        :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. If ``initial_bias`` is ``None``,\n            set to ``None``.\n\n    \"\"\"\n\n    def __init__(self, ndim, in_channels, out_channels, ksize, stride=1, pad=0,\n                 initialW=None, initial_bias=None, use_cudnn=True,\n                 cover_all=False):\n        ksize = conv_nd.as_tuple(ksize, ndim)\n        self.stride = stride\n        self.pad = pad\n        self.use_cudnn = use_cudnn\n        self.cover_all = cover_all\n\n        super(ConvolutionND, self).__init__()\n\n        W_shape = (out_channels, in_channels) + ksize\n        initialW = initializers._get_initializer(initialW)\n        self.add_param('W', W_shape, initializer=initialW)\n\n        if initial_bias is None:\n            self.b = None\n        else:\n            initial_bias = initializers._get_initializer(initial_bias)\n            self.add_param('b', out_channels, initializer=initial_bias)\n\n    def __call__(self, x):\n        \"\"\"Applies N-dimensional convolution layer.\n\n        Args:\n            x (~chainer.Variable): Input image.\n\n        Returns:\n            ~chainer.Variable: Output of convolution.\n\n        \"\"\"\n        return convolution_nd.convolution_nd(\n            x, self.W, self.b, self.stride, self.pad,\n            use_cudnn=self.use_cudnn, cover_all=self.cover_all)\n", "path": "chainer/links/connection/convolution_nd.py"}]}