prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  chainer.functions.Parameter cannot accept cupy.ndarray
  ```
  In [1]: import numpy, chainer, cupy
  In [2]: p = chainer.functions.Parameter(numpy.arange(12, dtype=numpy.float32))
  In [3]: p = chainer.functions.Parameter(cupy.arange(12, dtype=numpy.float32))
  ---------------------------------------------------------------------------
  ValueError                                Traceback (most recent call last)
  <ipython-input-3-3bee41ef9fca> in <module>()
  ----> 1 p = chainer.functions.Parameter(cupy.arange(12, dtype=numpy.float32))
  
  /home/delta/dev/chainer2/chainer/functions/connection/parameter.py in __init__(self, array)
       21     def __init__(self, array):
       22         self.W = array
  ---> 23         self.gW = numpy.full_like(array, numpy.nan)
       24 
       25     def __call__(self, volatile=False):
  
  /home/delta/.pyenv/versions/pyenv-2.7.9/lib/python2.7/site-packages/numpy/core/numeric.pyc in full_like(a, fill_value, dtype, order, subok)
      344 
      345     """
  --> 346     res = empty_like(a, dtype=dtype, order=order, subok=subok)
      347     multiarray.copyto(res, fill_value, casting='unsafe')
      348     return res
  
  ValueError: object __array__ method not producing an array
  ```
  
  
  --- 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/connection/parameter.py`
  Content:
  ```
  1 import numpy
  2 
  3 from chainer import function
  4 from chainer.utils import type_check
  5 
  6 
  7 class Parameter(function.Function):
  8 
  9     """Function that outputs its weight array.
  10 
  11     This is a parameterized function that takes no input and returns a variable
  12     holding a shallow copy of the parameter array.
  13 
  14     Args:
  15         array: Initial parameter array.
  16 
  17     """
  18     parameter_names = 'W',
  19     gradient_names = 'gW',
  20 
  21     def __init__(self, array):
  22         self.W = array
  23         self.gW = numpy.full_like(array, numpy.nan)
  24 
  25     def __call__(self, volatile=False):
  26         ret = super(Parameter, self).__call__()
  27         if volatile:
  28             ret.unchain_backward()
  29         ret.volatile = volatile
  30         return ret
  31 
  32     def check_type_forward(self, in_types):
  33         type_check.expect(in_types.size() == 0)
  34 
  35     def forward(self, x):
  36         return self.W,
  37 
  38     def backward(self, x, gy):
  39         self.gW += gy[0]
  40         return ()
  41
  ```
  --- 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/connection/parameter.py b/chainer/functions/connection/parameter.py
  --- a/chainer/functions/connection/parameter.py
  +++ b/chainer/functions/connection/parameter.py
  @@ -1,5 +1,6 @@
   import numpy
   
  +from chainer import cuda
   from chainer import function
   from chainer.utils import type_check
   
  @@ -20,7 +21,8 @@
   
       def __init__(self, array):
           self.W = array
  -        self.gW = numpy.full_like(array, numpy.nan)
  +        xp = cuda.get_array_module(array)
  +        self.gW = xp.full_like(self.W, numpy.nan)
   
       def __call__(self, volatile=False):
           ret = super(Parameter, self).__call__()
  
verification_info: {"golden_diff": "diff --git a/chainer/functions/connection/parameter.py b/chainer/functions/connection/parameter.py\n--- a/chainer/functions/connection/parameter.py\n+++ b/chainer/functions/connection/parameter.py\n@@ -1,5 +1,6 @@\n import numpy\n \n+from chainer import cuda\n from chainer import function\n from chainer.utils import type_check\n \n@@ -20,7 +21,8 @@\n \n     def __init__(self, array):\n         self.W = array\n-        self.gW = numpy.full_like(array, numpy.nan)\n+        xp = cuda.get_array_module(array)\n+        self.gW = xp.full_like(self.W, numpy.nan)\n \n     def __call__(self, volatile=False):\n         ret = super(Parameter, self).__call__()\n", "issue": "chainer.functions.Parameter cannot accept cupy.ndarray\n```\nIn [1]: import numpy, chainer, cupy\nIn [2]: p = chainer.functions.Parameter(numpy.arange(12, dtype=numpy.float32))\nIn [3]: p = chainer.functions.Parameter(cupy.arange(12, dtype=numpy.float32))\n---------------------------------------------------------------------------\nValueError                                Traceback (most recent call last)\n<ipython-input-3-3bee41ef9fca> in <module>()\n----> 1 p = chainer.functions.Parameter(cupy.arange(12, dtype=numpy.float32))\n\n/home/delta/dev/chainer2/chainer/functions/connection/parameter.py in __init__(self, array)\n     21     def __init__(self, array):\n     22         self.W = array\n---> 23         self.gW = numpy.full_like(array, numpy.nan)\n     24 \n     25     def __call__(self, volatile=False):\n\n/home/delta/.pyenv/versions/pyenv-2.7.9/lib/python2.7/site-packages/numpy/core/numeric.pyc in full_like(a, fill_value, dtype, order, subok)\n    344 \n    345     \"\"\"\n--> 346     res = empty_like(a, dtype=dtype, order=order, subok=subok)\n    347     multiarray.copyto(res, fill_value, casting='unsafe')\n    348     return res\n\nValueError: object __array__ method not producing an array\n```\n\n", "before_files": [{"content": "import numpy\n\nfrom chainer import function\nfrom chainer.utils import type_check\n\n\nclass Parameter(function.Function):\n\n    \"\"\"Function that outputs its weight array.\n\n    This is a parameterized function that takes no input and returns a variable\n    holding a shallow copy of the parameter array.\n\n    Args:\n        array: Initial parameter array.\n\n    \"\"\"\n    parameter_names = 'W',\n    gradient_names = 'gW',\n\n    def __init__(self, array):\n        self.W = array\n        self.gW = numpy.full_like(array, numpy.nan)\n\n    def __call__(self, volatile=False):\n        ret = super(Parameter, self).__call__()\n        if volatile:\n            ret.unchain_backward()\n        ret.volatile = volatile\n        return ret\n\n    def check_type_forward(self, in_types):\n        type_check.expect(in_types.size() == 0)\n\n    def forward(self, x):\n        return self.W,\n\n    def backward(self, x, gy):\n        self.gW += gy[0]\n        return ()\n", "path": "chainer/functions/connection/parameter.py"}], "after_files": [{"content": "import numpy\n\nfrom chainer import cuda\nfrom chainer import function\nfrom chainer.utils import type_check\n\n\nclass Parameter(function.Function):\n\n    \"\"\"Function that outputs its weight array.\n\n    This is a parameterized function that takes no input and returns a variable\n    holding a shallow copy of the parameter array.\n\n    Args:\n        array: Initial parameter array.\n\n    \"\"\"\n    parameter_names = 'W',\n    gradient_names = 'gW',\n\n    def __init__(self, array):\n        self.W = array\n        xp = cuda.get_array_module(array)\n        self.gW = xp.full_like(self.W, numpy.nan)\n\n    def __call__(self, volatile=False):\n        ret = super(Parameter, self).__call__()\n        if volatile:\n            ret.unchain_backward()\n        ret.volatile = volatile\n        return ret\n\n    def check_type_forward(self, in_types):\n        type_check.expect(in_types.size() == 0)\n\n    def forward(self, x):\n        return self.W,\n\n    def backward(self, x, gy):\n        self.gW += gy[0]\n        return ()\n", "path": "chainer/functions/connection/parameter.py"}]}