prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  `Function` which get no input cannot select `volatile`
  In `Function.__call__`, it checks `volatile` of all input variables, and the same value is used. But, `Function` which get not argument such as `Parameter`, always selects `volatile=False`. it may causes conflict of volatility.
  
  https://github.com/pfnet/chainer/blob/master/chainer/function.py#L132
  `Parameter` always returns `False` here.
  
  
  </issue>
  <code>
  [start of chainer/functions/parameter.py]
  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.empty_like(array)
  24 
  25     def check_type_forward(self, in_types):
  26         type_check.expect(in_types.size() == 0)
  27 
  28     def forward(self, x):
  29         return self.W,
  30 
  31     def backward(self, x, gy):
  32         self.gW += gy[0]
  33         return ()
  34
  [end of chainer/functions/parameter.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/chainer/functions/parameter.py b/chainer/functions/parameter.py
  --- a/chainer/functions/parameter.py
  +++ b/chainer/functions/parameter.py
  @@ -22,6 +22,13 @@
           self.W = array
           self.gW = numpy.empty_like(array)
   
  +    def __call__(self, volatile=False):
  +        ret = super(Parameter, self).__call__()
  +        if volatile:
  +            ret.unchain_backward()
  +        ret.volatile = volatile
  +        return ret
  +
       def check_type_forward(self, in_types):
           type_check.expect(in_types.size() == 0)
  
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@@ -22,6 +22,13 @@\n         self.W = array\n         self.gW = numpy.empty_like(array)\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", "issue": "`Function` which get no input cannot select `volatile`\nIn `Function.__call__`, it checks `volatile` of all input variables, and the same value is used. But, `Function` which get not argument such as `Parameter`, always selects `volatile=False`. it may causes conflict of volatility.\n\nhttps://github.com/pfnet/chainer/blob/master/chainer/function.py#L132\n`Parameter` always returns `False` here.\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.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"}]}