We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[document] The input argument name in `F.rrelu` is inconsistent
https://docs.chainer.org/en/stable/reference/generated/chainer.functions.rrelu.html#chainer.functions.rrelu

The slope for x < 0 is named `a` in the body text, but `r` is used for the input argument.

> Randomized Leaky Rectified Liner Unit function.
> This function is expressed as
> f(x)=max(x,ax),
> where a is a random number sampled from a uniform distribution U(l,u).
> ...
> ・r (N-dimensional array or None) – The r to be used for rrelu. The shape and dtype must be the same as x[0] and should be on the same device. If r is not specified or set to None, a r will be generated randomly according to the given l and u. If r is specified, l and u will be ignored.


--- 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/activation/rrelu.py`
Content:
```
1 import numpy as np
2 
3 import chainer
4 from chainer.backends import cuda
5 from chainer import function_node
6 from chainer.utils import argument
7 from chainer.utils import type_check
8 
9 
10 def _kern():
11     return cuda.elementwise(
12         'T cond, T x, T slope', 'T y',
13         'y = cond >= 0 ? x : (T)(slope * x)', 'rrelu')
14 
15 
16 class RReLU(function_node.FunctionNode):
17     """Randomized Leaky rectifier unit."""
18 
19     def __init__(self, lower=1. / 8, upper=1. / 3, r=None):
20         if not 0.0 <= lower < 1.0:
21             raise ValueError('lower must be in the range [0, 1)')
22         if not 0.0 <= upper < 1.0:
23             raise ValueError('upper must be in the range [0, 1)')
24         if not lower < upper:
25             raise ValueError('lower must be less than upper')
26         self.lower = lower
27         self.upper = upper
28         self.r = r
29 
30     def check_type_forward(self, in_types):
31         type_check.expect(in_types.size() == 1)
32         x_type, = in_types
33         type_check.expect(x_type.dtype.kind == 'f')
34         if self.r is not None:
35             type_check.expect(x_type.dtype == self.r.dtype)
36             type_check.expect(x_type.shape == self.r.shape)
37 
38     def forward_cpu(self, inputs):
39         x, = inputs
40         if chainer.config.train:
41             if self.r is None:
42                 self.r = np.random.uniform(
43                     self.lower, self.upper, x.shape
44                 ).astype(x.dtype, copy=False)
45         else:
46             self.r = np.full(
47                 x.shape, (self.lower + self.upper) / 2, dtype=x.dtype)
48         y = np.where(x >= 0, x, x * self.r)
49         self.retain_outputs((0,))
50         return y,
51 
52     def forward_gpu(self, inputs):
53         x, = inputs
54         xp = cuda.cupy
55         if chainer.config.train:
56             if self.r is None:
57                 self.r = xp.random.uniform(
58                     self.lower, self.upper, x.shape
59                 ).astype(x.dtype, copy=False)
60         else:
61             self.r = xp.full(
62                 x.shape, (self.lower + self.upper) / 2, dtype=x.dtype)
63         y = _kern()(x, x, self.r)
64         self.retain_outputs((0,))
65         return y,
66 
67     def backward(self, indexes, grad_outputs):
68         y = self.get_retained_outputs()[0].data
69         return _RReLUGrad(y, self.r).apply(grad_outputs)
70 
71 
72 class _RReLUGrad(function_node.FunctionNode):
73 
74     def __init__(self, y, r):
75         self.r = r
76         self.y = y
77 
78     def forward_cpu(self, inputs):
79         gy, = inputs
80         gy = np.where(self.y >= 0, gy, gy * self.r)
81         return gy,
82 
83     def forward_gpu(self, inputs):
84         gy, = inputs
85         gy = _kern()(self.y, gy, self.r)
86         return gy,
87 
88     def backward(self, indexes, grad_outputs):
89         return _RReLUGrad(self.y, self.r).apply(grad_outputs)
90 
91 
92 def rrelu(x, l=1. / 8, u=1. / 3, **kwargs):
93     """rrelu(x, l=1. / 8, u=1. / 3, *, r=None, return_r=False)
94 
95     Randomized Leaky Rectified Liner Unit function.
96 
97     This function is expressed as
98 
99     .. math:: f(x)=\\max(x, ax),
100 
101     where :math:`a` is a random number sampled from a uniform distribution
102     :math:`U(l, u)`.
103 
104     See: https://arxiv.org/pdf/1505.00853.pdf
105 
106     Args:
107         x (:class:`~chainer.Variable` or :ref:`ndarray`):
108             Input variable. A :math:`(s_1, s_2, ..., s_N)`-shaped float array.
109         l (float): The lower bound of the uniform distribution.
110         u (float): The upper bound of the uniform distribution.
111         r (:ref:`ndarray` or None):
112             The r to be used for rrelu.
113             The shape and dtype must be the same as ``x[0]`` and should be on
114             the same device.
115             If ``r``  is not specified or set to ``None``, a ``r`` will be
116             generated randomly according to the given ``l`` and ``u``.
117             If ``r`` is specified, ``l`` and ``u`` will be ignored.
118         return_r (bool):
119             If ``True``, the r used for rrelu is returned altogether with
120             the output variable.
121             The returned ``r`` can latter be reused by passing it to ``r``
122             argument.
123 
124     Returns:
125         ~chainer.Variable or tuple:
126             When ``return_r`` is ``False`` (default), return the output
127             variable. Otherwise returnes the tuple of the output variable and
128             ``r`` (:ref:`ndarray`). The ``r`` will be on the same device as
129             the input.
130             A :math:`(s_1, s_2, ..., s_N)`-shaped float array.
131 
132     .. admonition:: Example
133 
134         >>> x = np.array([[-1, 0], [2, -3], [-2, 1]], np.float32)
135         >>> x
136         array([[-1.,  0.],
137                [ 2., -3.],
138                [-2.,  1.]], dtype=float32)
139         >>> F.rrelu(x).array # doctest: +SKIP
140         array([[-0.24850948,  0.        ],
141                [ 2.        , -0.50844127],
142                [-0.598535  ,  1.        ]], dtype=float32)
143     """
144     r = None
145     return_r = False
146     if kwargs:
147         r, return_r = argument.parse_kwargs(
148             kwargs, ('r', r), ('return_r', r),
149             train='train argument is not supported anymore.'
150                   'Use chainer.using_config')
151 
152     func = RReLU(l, u, r)
153     out, = func.apply((x,))
154     r = func.r
155 
156     if return_r:
157         return out, r
158     return out
159
```
--- 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/functions/activation/rrelu.py b/chainer/functions/activation/rrelu.py
--- a/chainer/functions/activation/rrelu.py
+++ b/chainer/functions/activation/rrelu.py
@@ -96,12 +96,15 @@
 
     This function is expressed as
 
-    .. math:: f(x)=\\max(x, ax),
+    .. math:: f(x)=\\max(x, rx),
 
-    where :math:`a` is a random number sampled from a uniform distribution
+    where :math:`r` is a random number sampled from a uniform distribution
     :math:`U(l, u)`.
 
-    See: https://arxiv.org/pdf/1505.00853.pdf
+    .. note::
+
+        The :math:`r` corresponds to :math:`a` in the original
+        paper (https://arxiv.org/pdf/1505.00853.pdf).
 
     Args:
         x (:class:`~chainer.Variable` or :ref:`ndarray`):
@@ -112,7 +115,7 @@
             The r to be used for rrelu.
             The shape and dtype must be the same as ``x[0]`` and should be on
             the same device.
-            If ``r``  is not specified or set to ``None``, a ``r`` will be
+            If ``r``  is not specified or set to ``None``, an ``r`` will be
             generated randomly according to the given ``l`` and ``u``.
             If ``r`` is specified, ``l`` and ``u`` will be ignored.
         return_r (bool):

{"golden_diff": "diff --git a/chainer/functions/activation/rrelu.py b/chainer/functions/activation/rrelu.py\n--- a/chainer/functions/activation/rrelu.py\n+++ b/chainer/functions/activation/rrelu.py\n@@ -96,12 +96,15 @@\n \n     This function is expressed as\n \n-    .. math:: f(x)=\\\\max(x, ax),\n+    .. math:: f(x)=\\\\max(x, rx),\n \n-    where :math:`a` is a random number sampled from a uniform distribution\n+    where :math:`r` is a random number sampled from a uniform distribution\n     :math:`U(l, u)`.\n \n-    See: https://arxiv.org/pdf/1505.00853.pdf\n+    .. note::\n+\n+        The :math:`r` corresponds to :math:`a` in the original\n+        paper (https://arxiv.org/pdf/1505.00853.pdf).\n \n     Args:\n         x (:class:`~chainer.Variable` or :ref:`ndarray`):\n@@ -112,7 +115,7 @@\n             The r to be used for rrelu.\n             The shape and dtype must be the same as ``x[0]`` and should be on\n             the same device.\n-            If ``r``  is not specified or set to ``None``, a ``r`` will be\n+            If ``r``  is not specified or set to ``None``, an ``r`` will be\n             generated randomly according to the given ``l`` and ``u``.\n             If ``r`` is specified, ``l`` and ``u`` will be ignored.\n         return_r (bool):\n", "issue": "[document] The input argument name in `F.rrelu` is inconsistent\nhttps://docs.chainer.org/en/stable/reference/generated/chainer.functions.rrelu.html#chainer.functions.rrelu\r\n\r\nThe slope for x < 0 is named `a` in the body text, but `r` is used for the input argument.\r\n\r\n> Randomized Leaky Rectified Liner Unit function.\r\n> This function is expressed as\r\n> f(x)=max(x,ax),\r\n> where a is a random number sampled from a uniform distribution U(l,u).\r\n> ...\r\n> \u30fbr (N-dimensional array or None) \u2013 The r to be used for rrelu. The shape and dtype must be the same as x[0] and should be on the same device. If r is not specified or set to None, a r will be generated randomly according to the given l and u. If r is specified, l and u will be ignored.\r\n\n", "before_files": [{"content": "import numpy as np\n\nimport chainer\nfrom chainer.backends import cuda\nfrom chainer import function_node\nfrom chainer.utils import argument\nfrom chainer.utils import type_check\n\n\ndef _kern():\n    return cuda.elementwise(\n        'T cond, T x, T slope', 'T y',\n        'y = cond >= 0 ? x : (T)(slope * x)', 'rrelu')\n\n\nclass RReLU(function_node.FunctionNode):\n    \"\"\"Randomized Leaky rectifier unit.\"\"\"\n\n    def __init__(self, lower=1. / 8, upper=1. / 3, r=None):\n        if not 0.0 <= lower < 1.0:\n            raise ValueError('lower must be in the range [0, 1)')\n        if not 0.0 <= upper < 1.0:\n            raise ValueError('upper must be in the range [0, 1)')\n        if not lower < upper:\n            raise ValueError('lower must be less than upper')\n        self.lower = lower\n        self.upper = upper\n        self.r = r\n\n    def check_type_forward(self, in_types):\n        type_check.expect(in_types.size() == 1)\n        x_type, = in_types\n        type_check.expect(x_type.dtype.kind == 'f')\n        if self.r is not None:\n            type_check.expect(x_type.dtype == self.r.dtype)\n            type_check.expect(x_type.shape == self.r.shape)\n\n    def forward_cpu(self, inputs):\n        x, = inputs\n        if chainer.config.train:\n            if self.r is None:\n                self.r = np.random.uniform(\n                    self.lower, self.upper, x.shape\n                ).astype(x.dtype, copy=False)\n        else:\n            self.r = np.full(\n                x.shape, (self.lower + self.upper) / 2, dtype=x.dtype)\n        y = np.where(x >= 0, x, x * self.r)\n        self.retain_outputs((0,))\n        return y,\n\n    def forward_gpu(self, inputs):\n        x, = inputs\n        xp = cuda.cupy\n        if chainer.config.train:\n            if self.r is None:\n                self.r = xp.random.uniform(\n                    self.lower, self.upper, x.shape\n                ).astype(x.dtype, copy=False)\n        else:\n            self.r = xp.full(\n                x.shape, (self.lower + self.upper) / 2, dtype=x.dtype)\n        y = _kern()(x, x, self.r)\n        self.retain_outputs((0,))\n        return y,\n\n    def backward(self, indexes, grad_outputs):\n        y = self.get_retained_outputs()[0].data\n        return _RReLUGrad(y, self.r).apply(grad_outputs)\n\n\nclass _RReLUGrad(function_node.FunctionNode):\n\n    def __init__(self, y, r):\n        self.r = r\n        self.y = y\n\n    def forward_cpu(self, inputs):\n        gy, = inputs\n        gy = np.where(self.y >= 0, gy, gy * self.r)\n        return gy,\n\n    def forward_gpu(self, inputs):\n        gy, = inputs\n        gy = _kern()(self.y, gy, self.r)\n        return gy,\n\n    def backward(self, indexes, grad_outputs):\n        return _RReLUGrad(self.y, self.r).apply(grad_outputs)\n\n\ndef rrelu(x, l=1. / 8, u=1. / 3, **kwargs):\n    \"\"\"rrelu(x, l=1. / 8, u=1. / 3, *, r=None, return_r=False)\n\n    Randomized Leaky Rectified Liner Unit function.\n\n    This function is expressed as\n\n    .. math:: f(x)=\\\\max(x, ax),\n\n    where :math:`a` is a random number sampled from a uniform distribution\n    :math:`U(l, u)`.\n\n    See: https://arxiv.org/pdf/1505.00853.pdf\n\n    Args:\n        x (:class:`~chainer.Variable` or :ref:`ndarray`):\n            Input variable. A :math:`(s_1, s_2, ..., s_N)`-shaped float array.\n        l (float): The lower bound of the uniform distribution.\n        u (float): The upper bound of the uniform distribution.\n        r (:ref:`ndarray` or None):\n            The r to be used for rrelu.\n            The shape and dtype must be the same as ``x[0]`` and should be on\n            the same device.\n            If ``r``  is not specified or set to ``None``, a ``r`` will be\n            generated randomly according to the given ``l`` and ``u``.\n            If ``r`` is specified, ``l`` and ``u`` will be ignored.\n        return_r (bool):\n            If ``True``, the r used for rrelu is returned altogether with\n            the output variable.\n            The returned ``r`` can latter be reused by passing it to ``r``\n            argument.\n\n    Returns:\n        ~chainer.Variable or tuple:\n            When ``return_r`` is ``False`` (default), return the output\n            variable. Otherwise returnes the tuple of the output variable and\n            ``r`` (:ref:`ndarray`). The ``r`` will be on the same device as\n            the input.\n            A :math:`(s_1, s_2, ..., s_N)`-shaped float array.\n\n    .. admonition:: Example\n\n        >>> x = np.array([[-1, 0], [2, -3], [-2, 1]], np.float32)\n        >>> x\n        array([[-1.,  0.],\n               [ 2., -3.],\n               [-2.,  1.]], dtype=float32)\n        >>> F.rrelu(x).array # doctest: +SKIP\n        array([[-0.24850948,  0.        ],\n               [ 2.        , -0.50844127],\n               [-0.598535  ,  1.        ]], dtype=float32)\n    \"\"\"\n    r = None\n    return_r = False\n    if kwargs:\n        r, return_r = argument.parse_kwargs(\n            kwargs, ('r', r), ('return_r', r),\n            train='train argument is not supported anymore.'\n                  'Use chainer.using_config')\n\n    func = RReLU(l, u, r)\n    out, = func.apply((x,))\n    r = func.r\n\n    if return_r:\n        return out, r\n    return out\n", "path": "chainer/functions/activation/rrelu.py"}], "after_files": [{"content": "import numpy as np\n\nimport chainer\nfrom chainer.backends import cuda\nfrom chainer import function_node\nfrom chainer.utils import argument\nfrom chainer.utils import type_check\n\n\ndef _kern():\n    return cuda.elementwise(\n        'T cond, T x, T slope', 'T y',\n        'y = cond >= 0 ? x : (T)(slope * x)', 'rrelu')\n\n\nclass RReLU(function_node.FunctionNode):\n    \"\"\"Randomized Leaky rectifier unit.\"\"\"\n\n    def __init__(self, lower=1. / 8, upper=1. / 3, r=None):\n        if not 0.0 <= lower < 1.0:\n            raise ValueError('lower must be in the range [0, 1)')\n        if not 0.0 <= upper < 1.0:\n            raise ValueError('upper must be in the range [0, 1)')\n        if not lower < upper:\n            raise ValueError('lower must be less than upper')\n        self.lower = lower\n        self.upper = upper\n        self.r = r\n\n    def check_type_forward(self, in_types):\n        type_check.expect(in_types.size() == 1)\n        x_type, = in_types\n        type_check.expect(x_type.dtype.kind == 'f')\n        if self.r is not None:\n            type_check.expect(x_type.dtype == self.r.dtype)\n            type_check.expect(x_type.shape == self.r.shape)\n\n    def forward_cpu(self, inputs):\n        x, = inputs\n        if chainer.config.train:\n            if self.r is None:\n                self.r = np.random.uniform(\n                    self.lower, self.upper, x.shape\n                ).astype(x.dtype, copy=False)\n        else:\n            self.r = np.full(\n                x.shape, (self.lower + self.upper) / 2, dtype=x.dtype)\n        y = np.where(x >= 0, x, x * self.r)\n        self.retain_outputs((0,))\n        return y,\n\n    def forward_gpu(self, inputs):\n        x, = inputs\n        xp = cuda.cupy\n        if chainer.config.train:\n            if self.r is None:\n                self.r = xp.random.uniform(\n                    self.lower, self.upper, x.shape\n                ).astype(x.dtype, copy=False)\n        else:\n            self.r = xp.full(\n                x.shape, (self.lower + self.upper) / 2, dtype=x.dtype)\n        y = _kern()(x, x, self.r)\n        self.retain_outputs((0,))\n        return y,\n\n    def backward(self, indexes, grad_outputs):\n        y = self.get_retained_outputs()[0].data\n        return _RReLUGrad(y, self.r).apply(grad_outputs)\n\n\nclass _RReLUGrad(function_node.FunctionNode):\n\n    def __init__(self, y, r):\n        self.r = r\n        self.y = y\n\n    def forward_cpu(self, inputs):\n        gy, = inputs\n        gy = np.where(self.y >= 0, gy, gy * self.r)\n        return gy,\n\n    def forward_gpu(self, inputs):\n        gy, = inputs\n        gy = _kern()(self.y, gy, self.r)\n        return gy,\n\n    def backward(self, indexes, grad_outputs):\n        return _RReLUGrad(self.y, self.r).apply(grad_outputs)\n\n\ndef rrelu(x, l=1. / 8, u=1. / 3, **kwargs):\n    \"\"\"rrelu(x, l=1. / 8, u=1. / 3, *, r=None, return_r=False)\n\n    Randomized Leaky Rectified Liner Unit function.\n\n    This function is expressed as\n\n    .. math:: f(x)=\\\\max(x, rx),\n\n    where :math:`r` is a random number sampled from a uniform distribution\n    :math:`U(l, u)`.\n\n    .. note::\n\n        The :math:`r` corresponds to :math:`a` in the original\n        paper (https://arxiv.org/pdf/1505.00853.pdf).\n\n    Args:\n        x (:class:`~chainer.Variable` or :ref:`ndarray`):\n            Input variable. A :math:`(s_1, s_2, ..., s_N)`-shaped float array.\n        l (float): The lower bound of the uniform distribution.\n        u (float): The upper bound of the uniform distribution.\n        r (:ref:`ndarray` or None):\n            The r to be used for rrelu.\n            The shape and dtype must be the same as ``x[0]`` and should be on\n            the same device.\n            If ``r``  is not specified or set to ``None``, an ``r`` will be\n            generated randomly according to the given ``l`` and ``u``.\n            If ``r`` is specified, ``l`` and ``u`` will be ignored.\n        return_r (bool):\n            If ``True``, the r used for rrelu is returned altogether with\n            the output variable.\n            The returned ``r`` can latter be reused by passing it to ``r``\n            argument.\n\n    Returns:\n        ~chainer.Variable or tuple:\n            When ``return_r`` is ``False`` (default), return the output\n            variable. Otherwise returnes the tuple of the output variable and\n            ``r`` (:ref:`ndarray`). The ``r`` will be on the same device as\n            the input.\n            A :math:`(s_1, s_2, ..., s_N)`-shaped float array.\n\n    .. admonition:: Example\n\n        >>> x = np.array([[-1, 0], [2, -3], [-2, 1]], np.float32)\n        >>> x\n        array([[-1.,  0.],\n               [ 2., -3.],\n               [-2.,  1.]], dtype=float32)\n        >>> F.rrelu(x).array # doctest: +SKIP\n        array([[-0.24850948,  0.        ],\n               [ 2.        , -0.50844127],\n               [-0.598535  ,  1.        ]], dtype=float32)\n    \"\"\"\n    r = None\n    return_r = False\n    if kwargs:\n        r, return_r = argument.parse_kwargs(\n            kwargs, ('r', r), ('return_r', r),\n            train='train argument is not supported anymore.'\n                  'Use chainer.using_config')\n\n    func = RReLU(l, u, r)\n    out, = func.apply((x,))\n    r = func.r\n\n    if return_r:\n        return out, r\n    return out\n", "path": "chainer/functions/activation/rrelu.py"}]}