prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  SplitAxis should return a tuple with a single element
  `Function.__call__` returns a `Variable` when its forward function returns only one return value. When the number of return values depends on input values, such as `split_axis`, a user need to check the type of the return value of `Function`. A user needs to write a complicated source code.
  
  I have two design choice:
  - Only `SplitAxis` always returns a tuple of `Variable`s
  - Add keyword argument `force_tuple` to `Function.__call__`
  
  
  --- 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/array/split_axis.py`
  Content:
  ```
  1 import collections
  2 
  3 import six
  4 
  5 from chainer import cuda
  6 from chainer import function
  7 from chainer.utils import type_check
  8 
  9 
  10 class SplitAxis(function.Function):
  11 
  12     """Function that splits multiple arrays towards the specified axis."""
  13 
  14     def __init__(self, indices_or_sections, axis):
  15         if not isinstance(indices_or_sections, (int, collections.Iterable)):
  16             raise TypeError('indices_or_sections must be integer or 1-D array')
  17         self.indices_or_sections = indices_or_sections
  18         self.axis = axis
  19 
  20     def check_type_forward(self, in_types):
  21         type_check.expect(in_types.size() == 1)
  22         type_check.expect(in_types[0].ndim > self.axis)
  23 
  24         if isinstance(self.indices_or_sections, collections.Iterable):
  25             max_index = type_check.Variable(
  26                 self.indices_or_sections[-1], 'max_index')
  27             type_check.expect(in_types[0].shape[self.axis] > max_index)
  28         else:
  29             sections = type_check.Variable(
  30                 self.indices_or_sections, 'sections')
  31             type_check.expect(in_types[0].shape[self.axis] % sections == 0)
  32 
  33     def forward(self, x):
  34         if isinstance(self.indices_or_sections, collections.Iterable):
  35             cdimx = x[0].shape[self.axis]
  36             ind = list(self.indices_or_sections)
  37             ind.append(cdimx)
  38             prev_i = 0
  39             for i in ind:
  40                 cdimy = max(0, min(i, cdimx) - prev_i)
  41                 if cdimy == 0:
  42                     raise ValueError('Not support if shape contains 0')
  43                 prev_i = i
  44         xp = cuda.get_array_module(*x)
  45         return tuple(xp.split(x[0], self.indices_or_sections, self.axis))
  46 
  47     def backward(self, x, gys):
  48         xp = cuda.get_array_module(*x)
  49         if any(gy is None for gy in gys):
  50             gx = xp.zeros_like(x[0])
  51             gxs = xp.split(gx, self.indices_or_sections, self.axis)
  52             for gxi, gy in six.moves.zip(gxs, gys):
  53                 if gy is None:
  54                     continue
  55                 gxi[:] = gy
  56             return gx,
  57         else:
  58             return xp.concatenate(gys, axis=self.axis),
  59 
  60 
  61 def split_axis(x, indices_or_sections, axis):
  62     """Splits given variables along an axis.
  63 
  64     Args:
  65         x (tuple of Variables): Variables to be split.
  66         indices_or_sections (int or 1-D array): If this argument is an integer,
  67             N, the array will be divided into N equal arrays along axis.
  68             If it is a 1-D array of sorted integers, it
  69             indicates the positions where the array is split.
  70         axis (int): Axis that the input array is split along.
  71 
  72     Returns:
  73         tuple or Variable: Tuple of :class:`~chainer.Variable` objects
  74              if the number of outputs is more than 1 or
  75              :class:`~chainer.Variable` otherwise.
  76 
  77     .. note::
  78         This function raises :class:`ValueError` if at least
  79         one of the outputs is split to zero-size
  80         (i.e. `axis`-th value of its shape is zero).
  81 
  82     """
  83     return SplitAxis(indices_or_sections, axis)(x)
  84
  ```
  --- 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/array/split_axis.py b/chainer/functions/array/split_axis.py
  --- a/chainer/functions/array/split_axis.py
  +++ b/chainer/functions/array/split_axis.py
  @@ -2,6 +2,7 @@
   
   import six
   
  +import chainer
   from chainer import cuda
   from chainer import function
   from chainer.utils import type_check
  @@ -58,7 +59,7 @@
               return xp.concatenate(gys, axis=self.axis),
   
   
  -def split_axis(x, indices_or_sections, axis):
  +def split_axis(x, indices_or_sections, axis, force_tuple=False):
       """Splits given variables along an axis.
   
       Args:
  @@ -68,16 +69,23 @@
               If it is a 1-D array of sorted integers, it
               indicates the positions where the array is split.
           axis (int): Axis that the input array is split along.
  +        force_tuple (bool): If ``True``, this method returns a tuple even when
  +            the number of outputs is one.
   
       Returns:
           tuple or Variable: Tuple of :class:`~chainer.Variable` objects
                if the number of outputs is more than 1 or
                :class:`~chainer.Variable` otherwise.
  +             When ``force_tuple`` is ``True``, returned value is always a tuple
  +             regardless of the number of outputs.
   
       .. note::
           This function raises :class:`ValueError` if at least
           one of the outputs is split to zero-size
  -        (i.e. `axis`-th value of its shape is zero).
  +        (i.e. ``axis``-th value of its shape is zero).
   
       """
  -    return SplitAxis(indices_or_sections, axis)(x)
  +    res = SplitAxis(indices_or_sections, axis)(x)
  +    if force_tuple and isinstance(res, chainer.Variable):
  +        res = (res,)
  +    return res
  
verification_info: {"golden_diff": "diff --git a/chainer/functions/array/split_axis.py b/chainer/functions/array/split_axis.py\n--- a/chainer/functions/array/split_axis.py\n+++ b/chainer/functions/array/split_axis.py\n@@ -2,6 +2,7 @@\n \n import six\n \n+import chainer\n from chainer import cuda\n from chainer import function\n from chainer.utils import type_check\n@@ -58,7 +59,7 @@\n             return xp.concatenate(gys, axis=self.axis),\n \n \n-def split_axis(x, indices_or_sections, axis):\n+def split_axis(x, indices_or_sections, axis, force_tuple=False):\n     \"\"\"Splits given variables along an axis.\n \n     Args:\n@@ -68,16 +69,23 @@\n             If it is a 1-D array of sorted integers, it\n             indicates the positions where the array is split.\n         axis (int): Axis that the input array is split along.\n+        force_tuple (bool): If ``True``, this method returns a tuple even when\n+            the number of outputs is one.\n \n     Returns:\n         tuple or Variable: Tuple of :class:`~chainer.Variable` objects\n              if the number of outputs is more than 1 or\n              :class:`~chainer.Variable` otherwise.\n+             When ``force_tuple`` is ``True``, returned value is always a tuple\n+             regardless of the number of outputs.\n \n     .. note::\n         This function raises :class:`ValueError` if at least\n         one of the outputs is split to zero-size\n-        (i.e. `axis`-th value of its shape is zero).\n+        (i.e. ``axis``-th value of its shape is zero).\n \n     \"\"\"\n-    return SplitAxis(indices_or_sections, axis)(x)\n+    res = SplitAxis(indices_or_sections, axis)(x)\n+    if force_tuple and isinstance(res, chainer.Variable):\n+        res = (res,)\n+    return res\n", "issue": "SplitAxis should return a tuple with a single element\n`Function.__call__` returns a `Variable` when its forward function returns only one return value. When the number of return values depends on input values, such as `split_axis`, a user need to check the type of the return value of `Function`. A user needs to write a complicated source code.\n\nI have two design choice:\n- Only `SplitAxis` always returns a tuple of `Variable`s\n- Add keyword argument `force_tuple` to `Function.__call__`\n\n", "before_files": [{"content": "import collections\n\nimport six\n\nfrom chainer import cuda\nfrom chainer import function\nfrom chainer.utils import type_check\n\n\nclass SplitAxis(function.Function):\n\n    \"\"\"Function that splits multiple arrays towards the specified axis.\"\"\"\n\n    def __init__(self, indices_or_sections, axis):\n        if not isinstance(indices_or_sections, (int, collections.Iterable)):\n            raise TypeError('indices_or_sections must be integer or 1-D array')\n        self.indices_or_sections = indices_or_sections\n        self.axis = axis\n\n    def check_type_forward(self, in_types):\n        type_check.expect(in_types.size() == 1)\n        type_check.expect(in_types[0].ndim > self.axis)\n\n        if isinstance(self.indices_or_sections, collections.Iterable):\n            max_index = type_check.Variable(\n                self.indices_or_sections[-1], 'max_index')\n            type_check.expect(in_types[0].shape[self.axis] > max_index)\n        else:\n            sections = type_check.Variable(\n                self.indices_or_sections, 'sections')\n            type_check.expect(in_types[0].shape[self.axis] % sections == 0)\n\n    def forward(self, x):\n        if isinstance(self.indices_or_sections, collections.Iterable):\n            cdimx = x[0].shape[self.axis]\n            ind = list(self.indices_or_sections)\n            ind.append(cdimx)\n            prev_i = 0\n            for i in ind:\n                cdimy = max(0, min(i, cdimx) - prev_i)\n                if cdimy == 0:\n                    raise ValueError('Not support if shape contains 0')\n                prev_i = i\n        xp = cuda.get_array_module(*x)\n        return tuple(xp.split(x[0], self.indices_or_sections, self.axis))\n\n    def backward(self, x, gys):\n        xp = cuda.get_array_module(*x)\n        if any(gy is None for gy in gys):\n            gx = xp.zeros_like(x[0])\n            gxs = xp.split(gx, self.indices_or_sections, self.axis)\n            for gxi, gy in six.moves.zip(gxs, gys):\n                if gy is None:\n                    continue\n                gxi[:] = gy\n            return gx,\n        else:\n            return xp.concatenate(gys, axis=self.axis),\n\n\ndef split_axis(x, indices_or_sections, axis):\n    \"\"\"Splits given variables along an axis.\n\n    Args:\n        x (tuple of Variables): Variables to be split.\n        indices_or_sections (int or 1-D array): If this argument is an integer,\n            N, the array will be divided into N equal arrays along axis.\n            If it is a 1-D array of sorted integers, it\n            indicates the positions where the array is split.\n        axis (int): Axis that the input array is split along.\n\n    Returns:\n        tuple or Variable: Tuple of :class:`~chainer.Variable` objects\n             if the number of outputs is more than 1 or\n             :class:`~chainer.Variable` otherwise.\n\n    .. note::\n        This function raises :class:`ValueError` if at least\n        one of the outputs is split to zero-size\n        (i.e. `axis`-th value of its shape is zero).\n\n    \"\"\"\n    return SplitAxis(indices_or_sections, axis)(x)\n", "path": "chainer/functions/array/split_axis.py"}], "after_files": [{"content": "import collections\n\nimport six\n\nimport chainer\nfrom chainer import cuda\nfrom chainer import function\nfrom chainer.utils import type_check\n\n\nclass SplitAxis(function.Function):\n\n    \"\"\"Function that splits multiple arrays towards the specified axis.\"\"\"\n\n    def __init__(self, indices_or_sections, axis):\n        if not isinstance(indices_or_sections, (int, collections.Iterable)):\n            raise TypeError('indices_or_sections must be integer or 1-D array')\n        self.indices_or_sections = indices_or_sections\n        self.axis = axis\n\n    def check_type_forward(self, in_types):\n        type_check.expect(in_types.size() == 1)\n        type_check.expect(in_types[0].ndim > self.axis)\n\n        if isinstance(self.indices_or_sections, collections.Iterable):\n            max_index = type_check.Variable(\n                self.indices_or_sections[-1], 'max_index')\n            type_check.expect(in_types[0].shape[self.axis] > max_index)\n        else:\n            sections = type_check.Variable(\n                self.indices_or_sections, 'sections')\n            type_check.expect(in_types[0].shape[self.axis] % sections == 0)\n\n    def forward(self, x):\n        if isinstance(self.indices_or_sections, collections.Iterable):\n            cdimx = x[0].shape[self.axis]\n            ind = list(self.indices_or_sections)\n            ind.append(cdimx)\n            prev_i = 0\n            for i in ind:\n                cdimy = max(0, min(i, cdimx) - prev_i)\n                if cdimy == 0:\n                    raise ValueError('Not support if shape contains 0')\n                prev_i = i\n        xp = cuda.get_array_module(*x)\n        return tuple(xp.split(x[0], self.indices_or_sections, self.axis))\n\n    def backward(self, x, gys):\n        xp = cuda.get_array_module(*x)\n        if any(gy is None for gy in gys):\n            gx = xp.zeros_like(x[0])\n            gxs = xp.split(gx, self.indices_or_sections, self.axis)\n            for gxi, gy in six.moves.zip(gxs, gys):\n                if gy is None:\n                    continue\n                gxi[:] = gy\n            return gx,\n        else:\n            return xp.concatenate(gys, axis=self.axis),\n\n\ndef split_axis(x, indices_or_sections, axis, force_tuple=False):\n    \"\"\"Splits given variables along an axis.\n\n    Args:\n        x (tuple of Variables): Variables to be split.\n        indices_or_sections (int or 1-D array): If this argument is an integer,\n            N, the array will be divided into N equal arrays along axis.\n            If it is a 1-D array of sorted integers, it\n            indicates the positions where the array is split.\n        axis (int): Axis that the input array is split along.\n        force_tuple (bool): If ``True``, this method returns a tuple even when\n            the number of outputs is one.\n\n    Returns:\n        tuple or Variable: Tuple of :class:`~chainer.Variable` objects\n             if the number of outputs is more than 1 or\n             :class:`~chainer.Variable` otherwise.\n             When ``force_tuple`` is ``True``, returned value is always a tuple\n             regardless of the number of outputs.\n\n    .. note::\n        This function raises :class:`ValueError` if at least\n        one of the outputs is split to zero-size\n        (i.e. ``axis``-th value of its shape is zero).\n\n    \"\"\"\n    res = SplitAxis(indices_or_sections, axis)(x)\n    if force_tuple and isinstance(res, chainer.Variable):\n        res = (res,)\n    return res\n", "path": "chainer/functions/array/split_axis.py"}]}