We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN 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.

--- 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/optimizer_hooks/gradient_hard_clipping.py`
Content:
```
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 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()

```

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`

{"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"}], "after_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"}]}