prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Refactor utility link in optimizer_hooks unit tests
  `chainer_tests/optimizer_hooks_tests` uses similar dummy links, which can be refactored to reduce repetition.
  
  </issue>
  <code>
  [start of chainer/optimizer_hooks/gradient_hard_clipping.py]
  1 import chainer
  2 from chainer import backend
  3 
  4 
  5 class GradientHardClipping(object):
  6 
  7     """Optimizer/UpdateRule hook function for gradient clipping.
  8 
  9     This hook function clips all gradient arrays to be within a lower and upper
  10     bound.
  11 
  12     Args:
  13         lower_bound (float): The lower bound of the gradient value.
  14         upper_bound (float): The upper bound of the gradient value.
  15 
  16     Attributes:
  17         ~optimizer_hooks.GradientHardClipping.lower_bound (float): The
  18                          lower bound of the gradient value.
  19         ~optimizer_hooks.GradientHardClipping.upper_bound (float): The
  20                          upper bound of the gradient value.
  21         ~optimizer_hooks.GradientHardClipping.timing (string): Specifies
  22                          when this hook should be called by the
  23                          Optimizer/UpdateRule. Valid values are 'pre'
  24                          (before any updates) and 'post'
  25                          (after any updates).
  26         ~optimizer_hooks.GradientHardClipping.call_for_each_param (bool): \
  27                          Specifies if this hook is called for each parameter
  28                          (``True``) or only once (``False``) by an optimizer to
  29                          which this hook is registered. This function does
  30                          not expect users to switch the value from default one,
  31                          which is `True`.
  32 
  33     .. versionadded:: 4.0.0
  34        The *timing* parameter.
  35 
  36     """
  37     name = 'GradientHardClipping'
  38     call_for_each_param = True
  39     timing = 'pre'
  40 
  41     def __init__(self, lower_bound, upper_bound):
  42         self.lower_bound = lower_bound
  43         self.upper_bound = upper_bound
  44 
  45     def __call__(self, rule, param):
  46         grad = param.grad
  47         if grad is None:
  48             return
  49         with chainer.using_device(param.device):
  50             xp = param.device.xp
  51             # TODO(kshitij12345): Fix when chainerx.clip
  52             # supports kwarg `out`.
  53             if xp == backend.chainerx \
  54                     or isinstance(param.grad, backend.intel64.mdarray):
  55                 grad[:] = grad.clip(self.lower_bound, self.upper_bound)
  56             else:
  57                 # Save on new object allocation when using numpy and cupy
  58                 # using kwarg `out`
  59                 xp.clip(grad, self.lower_bound, self.upper_bound, out=grad)
  60
  [end of chainer/optimizer_hooks/gradient_hard_clipping.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/optimizer_hooks/gradient_hard_clipping.py b/chainer/optimizer_hooks/gradient_hard_clipping.py
  --- a/chainer/optimizer_hooks/gradient_hard_clipping.py
  +++ b/chainer/optimizer_hooks/gradient_hard_clipping.py
  @@ -52,7 +52,7 @@
               # supports kwarg `out`.
               if xp == backend.chainerx \
                       or isinstance(param.grad, backend.intel64.mdarray):
  -                grad[:] = grad.clip(self.lower_bound, self.upper_bound)
  +                grad[...] = grad.clip(self.lower_bound, self.upper_bound)
               else:
                   # Save on new object allocation when using numpy and cupy
                   # using kwarg `out`
  
verification_info: {"golden_diff": "diff --git a/chainer/optimizer_hooks/gradient_hard_clipping.py b/chainer/optimizer_hooks/gradient_hard_clipping.py\n--- a/chainer/optimizer_hooks/gradient_hard_clipping.py\n+++ b/chainer/optimizer_hooks/gradient_hard_clipping.py\n@@ -52,7 +52,7 @@\n             # supports kwarg `out`.\n             if xp == backend.chainerx \\\n                     or isinstance(param.grad, backend.intel64.mdarray):\n-                grad[:] = grad.clip(self.lower_bound, self.upper_bound)\n+                grad[...] = grad.clip(self.lower_bound, self.upper_bound)\n             else:\n                 # Save on new object allocation when using numpy and cupy\n                 # using kwarg `out`\n", "issue": "Refactor utility link in optimizer_hooks unit tests\n`chainer_tests/optimizer_hooks_tests` uses similar dummy links, which can be refactored to reduce repetition.\n", "before_files": [{"content": "import chainer\nfrom chainer import backend\n\n\nclass GradientHardClipping(object):\n\n    \"\"\"Optimizer/UpdateRule hook function for gradient clipping.\n\n    This hook function clips all gradient arrays to be within a lower and upper\n    bound.\n\n    Args:\n        lower_bound (float): The lower bound of the gradient value.\n        upper_bound (float): The upper bound of the gradient value.\n\n    Attributes:\n        ~optimizer_hooks.GradientHardClipping.lower_bound (float): The\n                         lower bound of the gradient value.\n        ~optimizer_hooks.GradientHardClipping.upper_bound (float): The\n                         upper bound of the gradient value.\n        ~optimizer_hooks.GradientHardClipping.timing (string): Specifies\n                         when this hook should be called by the\n                         Optimizer/UpdateRule. Valid values are 'pre'\n                         (before any updates) and 'post'\n                         (after any updates).\n        ~optimizer_hooks.GradientHardClipping.call_for_each_param (bool): \\\n                         Specifies if this hook is called for each parameter\n                         (``True``) or only once (``False``) by an optimizer to\n                         which this hook is registered. This function does\n                         not expect users to switch the value from default one,\n                         which is `True`.\n\n    .. versionadded:: 4.0.0\n       The *timing* parameter.\n\n    \"\"\"\n    name = 'GradientHardClipping'\n    call_for_each_param = True\n    timing = 'pre'\n\n    def __init__(self, lower_bound, upper_bound):\n        self.lower_bound = lower_bound\n        self.upper_bound = upper_bound\n\n    def __call__(self, rule, param):\n        grad = param.grad\n        if grad is None:\n            return\n        with chainer.using_device(param.device):\n            xp = param.device.xp\n            # TODO(kshitij12345): Fix when chainerx.clip\n            # supports kwarg `out`.\n            if xp == backend.chainerx \\\n                    or isinstance(param.grad, backend.intel64.mdarray):\n                grad[:] = grad.clip(self.lower_bound, self.upper_bound)\n            else:\n                # Save on new object allocation when using numpy and cupy\n                # using kwarg `out`\n                xp.clip(grad, self.lower_bound, self.upper_bound, out=grad)\n", "path": "chainer/optimizer_hooks/gradient_hard_clipping.py"}]}