prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Add type check to Parameter function
  Related to #123 
  
  
  --- 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/parameter.py`
  Content:
  ```
  1 import numpy
  2 
  3 from chainer import function
  4 
  5 
  6 class Parameter(function.Function):
  7 
  8     """Function that outputs its weight array.
  9 
  10     This is a parameterized function that takes no input and returns a variable
  11     holding a shallow copy of the parameter array.
  12 
  13     Args:
  14         array: Initial parameter array.
  15 
  16     """
  17     parameter_names = 'W',
  18     gradient_names = 'gW',
  19 
  20     def __init__(self, array):
  21         self.W = array
  22         self.gW = numpy.empty_like(array)
  23 
  24     def forward(self, x):
  25         return self.W,
  26 
  27     def backward(self, x, gy):
  28         self.gW += gy[0]
  29         return ()
  30
  ```
  --- 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/parameter.py b/chainer/functions/parameter.py
  --- a/chainer/functions/parameter.py
  +++ b/chainer/functions/parameter.py
  @@ -1,6 +1,7 @@
   import numpy
   
   from chainer import function
  +from chainer.utils import type_check
   
   
   class Parameter(function.Function):
  @@ -21,6 +22,9 @@
           self.W = array
           self.gW = numpy.empty_like(array)
   
  +    def check_type_forward(self, in_types):
  +        type_check.expect(in_types.size() == 0)
  +
       def forward(self, x):
           return self.W,
  
verification_info: {"golden_diff": "diff --git a/chainer/functions/parameter.py b/chainer/functions/parameter.py\n--- a/chainer/functions/parameter.py\n+++ b/chainer/functions/parameter.py\n@@ -1,6 +1,7 @@\n import numpy\n \n from chainer import function\n+from chainer.utils import type_check\n \n \n class Parameter(function.Function):\n@@ -21,6 +22,9 @@\n         self.W = array\n         self.gW = numpy.empty_like(array)\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", "issue": "Add type check to Parameter function\nRelated to #123 \n\n", "before_files": [{"content": "import numpy\n\nfrom chainer import function\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.empty_like(array)\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/parameter.py"}], "after_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.empty_like(array)\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/parameter.py"}]}