prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Mix CPU mode and GPU mode in one network
  I want to use CPU mode for some functions and GPU mode for others in one network.
  When I use a large number of vocabulary in EmbedID about >=1,000,000 words, it consumes large size of memory in GPU. In this situation, I need to use EmbedID in CPU, and to use other functions like LSTMs in GPU.
  
  
  --- 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/functions/array/copy.py`
  Content:
  ```
  1 from chainer import cuda
  2 from chainer import function
  3 from chainer.utils import type_check
  4 
  5 
  6 class Copy(function.Function):
  7 
  8     """Copy an input :class:`cupy.ndarray` onto another device."""
  9 
  10     def __init__(self, out_device):
  11         self.out_device = out_device
  12 
  13     def check_type_forward(self, in_types):
  14         type_check.expect(
  15             in_types.size() == 1
  16         )
  17 
  18     def forward_cpu(self, x):
  19         return x[0].copy(),
  20 
  21     def forward_gpu(self, x):
  22         return cuda.copy(x[0], out_device=self.out_device),
  23 
  24     def backward_cpu(self, x, gy):
  25         return gy[0].copy(),
  26 
  27     def backward_gpu(self, x, gy):
  28         return cuda.copy(gy[0], out_device=cuda.get_device(x[0])),
  29 
  30 
  31 def copy(x, dst):
  32     """Copies the input variable onto the specified device.
  33 
  34     This function copies the array of input variable onto the device specified
  35     by ``dst`` if the original array is on GPU, and otherwise just copies the
  36     array within host memory.
  37 
  38     Args:
  39         x (~chainer.Variable): Variable to be copied.
  40         dst: Target device specifier.
  41 
  42     Returns:
  43         ~chainer.Variable: Output variable.
  44 
  45     """
  46     return Copy(dst)(x)
  47
  ```
  --- 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/functions/array/copy.py b/chainer/functions/array/copy.py
  --- a/chainer/functions/array/copy.py
  +++ b/chainer/functions/array/copy.py
  @@ -16,24 +16,45 @@
           )
   
       def forward_cpu(self, x):
  -        return x[0].copy(),
  +        if self.out_device == -1:
  +            return x[0].copy(),
  +        else:
  +            return cuda.to_gpu(x[0], device=self.out_device),
   
       def forward_gpu(self, x):
  -        return cuda.copy(x[0], out_device=self.out_device),
  +        if self.out_device == -1:
  +            return cuda.to_cpu(x[0]),
  +        else:
  +            return cuda.copy(x[0], out_device=self.out_device),
  +
  +    def backward(self, inputs, grad_outputs):
  +        # In this function, `grad_outputs` contains cuda arrays even when
  +        # `inputs` only contains numpy arrays.
  +        if isinstance(inputs[0], cuda.ndarray):
  +            return self.backward_gpu(inputs, grad_outputs)
  +        else:
  +            return self.backward_cpu(inputs, grad_outputs)
   
       def backward_cpu(self, x, gy):
  -        return gy[0].copy(),
  +        if self.out_device == -1:
  +            return gy[0].copy(),
  +        else:
  +            return cuda.to_cpu(gy[0]),
   
       def backward_gpu(self, x, gy):
  -        return cuda.copy(gy[0], out_device=cuda.get_device(x[0])),
  +        if self.out_device == -1:
  +            return cuda.to_gpu(gy[0], device=cuda.get_device(x[0])),
  +        else:
  +            return cuda.copy(gy[0], out_device=cuda.get_device(x[0])),
   
   
   def copy(x, dst):
       """Copies the input variable onto the specified device.
   
       This function copies the array of input variable onto the device specified
  -    by ``dst`` if the original array is on GPU, and otherwise just copies the
  -    array within host memory.
  +    by ``dst``. When ``dst == -1``, it copies the array onto the host memory.
  +    This function supports copies from host to device, from device to device
  +    and from device to host.
   
       Args:
           x (~chainer.Variable): Variable to be copied.
  
verification_info: {"golden_diff": "diff --git a/chainer/functions/array/copy.py b/chainer/functions/array/copy.py\n--- a/chainer/functions/array/copy.py\n+++ b/chainer/functions/array/copy.py\n@@ -16,24 +16,45 @@\n         )\n \n     def forward_cpu(self, x):\n-        return x[0].copy(),\n+        if self.out_device == -1:\n+            return x[0].copy(),\n+        else:\n+            return cuda.to_gpu(x[0], device=self.out_device),\n \n     def forward_gpu(self, x):\n-        return cuda.copy(x[0], out_device=self.out_device),\n+        if self.out_device == -1:\n+            return cuda.to_cpu(x[0]),\n+        else:\n+            return cuda.copy(x[0], out_device=self.out_device),\n+\n+    def backward(self, inputs, grad_outputs):\n+        # In this function, `grad_outputs` contains cuda arrays even when\n+        # `inputs` only contains numpy arrays.\n+        if isinstance(inputs[0], cuda.ndarray):\n+            return self.backward_gpu(inputs, grad_outputs)\n+        else:\n+            return self.backward_cpu(inputs, grad_outputs)\n \n     def backward_cpu(self, x, gy):\n-        return gy[0].copy(),\n+        if self.out_device == -1:\n+            return gy[0].copy(),\n+        else:\n+            return cuda.to_cpu(gy[0]),\n \n     def backward_gpu(self, x, gy):\n-        return cuda.copy(gy[0], out_device=cuda.get_device(x[0])),\n+        if self.out_device == -1:\n+            return cuda.to_gpu(gy[0], device=cuda.get_device(x[0])),\n+        else:\n+            return cuda.copy(gy[0], out_device=cuda.get_device(x[0])),\n \n \n def copy(x, dst):\n     \"\"\"Copies the input variable onto the specified device.\n \n     This function copies the array of input variable onto the device specified\n-    by ``dst`` if the original array is on GPU, and otherwise just copies the\n-    array within host memory.\n+    by ``dst``. When ``dst == -1``, it copies the array onto the host memory.\n+    This function supports copies from host to device, from device to device\n+    and from device to host.\n \n     Args:\n         x (~chainer.Variable): Variable to be copied.\n", "issue": "Mix CPU mode and GPU mode in one network\nI want to use CPU mode for some functions and GPU mode for others in one network.\nWhen I use a large number of vocabulary in EmbedID about >=1,000,000 words, it consumes large size of memory in GPU. In this situation, I need to use EmbedID in CPU, and to use other functions like LSTMs in GPU.\n\n", "before_files": [{"content": "from chainer import cuda\nfrom chainer import function\nfrom chainer.utils import type_check\n\n\nclass Copy(function.Function):\n\n    \"\"\"Copy an input :class:`cupy.ndarray` onto another device.\"\"\"\n\n    def __init__(self, out_device):\n        self.out_device = out_device\n\n    def check_type_forward(self, in_types):\n        type_check.expect(\n            in_types.size() == 1\n        )\n\n    def forward_cpu(self, x):\n        return x[0].copy(),\n\n    def forward_gpu(self, x):\n        return cuda.copy(x[0], out_device=self.out_device),\n\n    def backward_cpu(self, x, gy):\n        return gy[0].copy(),\n\n    def backward_gpu(self, x, gy):\n        return cuda.copy(gy[0], out_device=cuda.get_device(x[0])),\n\n\ndef copy(x, dst):\n    \"\"\"Copies the input variable onto the specified device.\n\n    This function copies the array of input variable onto the device specified\n    by ``dst`` if the original array is on GPU, and otherwise just copies the\n    array within host memory.\n\n    Args:\n        x (~chainer.Variable): Variable to be copied.\n        dst: Target device specifier.\n\n    Returns:\n        ~chainer.Variable: Output variable.\n\n    \"\"\"\n    return Copy(dst)(x)\n", "path": "chainer/functions/array/copy.py"}], "after_files": [{"content": "from chainer import cuda\nfrom chainer import function\nfrom chainer.utils import type_check\n\n\nclass Copy(function.Function):\n\n    \"\"\"Copy an input :class:`cupy.ndarray` onto another device.\"\"\"\n\n    def __init__(self, out_device):\n        self.out_device = out_device\n\n    def check_type_forward(self, in_types):\n        type_check.expect(\n            in_types.size() == 1\n        )\n\n    def forward_cpu(self, x):\n        if self.out_device == -1:\n            return x[0].copy(),\n        else:\n            return cuda.to_gpu(x[0], device=self.out_device),\n\n    def forward_gpu(self, x):\n        if self.out_device == -1:\n            return cuda.to_cpu(x[0]),\n        else:\n            return cuda.copy(x[0], out_device=self.out_device),\n\n    def backward(self, inputs, grad_outputs):\n        # In this function, `grad_outputs` contains cuda arrays even when\n        # `inputs` only contains numpy arrays.\n        if isinstance(inputs[0], cuda.ndarray):\n            return self.backward_gpu(inputs, grad_outputs)\n        else:\n            return self.backward_cpu(inputs, grad_outputs)\n\n    def backward_cpu(self, x, gy):\n        if self.out_device == -1:\n            return gy[0].copy(),\n        else:\n            return cuda.to_cpu(gy[0]),\n\n    def backward_gpu(self, x, gy):\n        if self.out_device == -1:\n            return cuda.to_gpu(gy[0], device=cuda.get_device(x[0])),\n        else:\n            return cuda.copy(gy[0], out_device=cuda.get_device(x[0])),\n\n\ndef copy(x, dst):\n    \"\"\"Copies the input variable onto the specified device.\n\n    This function copies the array of input variable onto the device specified\n    by ``dst``. When ``dst == -1``, it copies the array onto the host memory.\n    This function supports copies from host to device, from device to device\n    and from device to host.\n\n    Args:\n        x (~chainer.Variable): Variable to be copied.\n        dst: Target device specifier.\n\n    Returns:\n        ~chainer.Variable: Output variable.\n\n    \"\"\"\n    return Copy(dst)(x)\n", "path": "chainer/functions/array/copy.py"}]}