We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
`split_axis` doesn't support empty sections
This code causes a TypeError.
`functions.split_axis(x, [], 0)`


--- 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 import chainer
6 from chainer import cuda
7 from chainer import function
8 from chainer.utils import type_check
9 
10 
11 class SplitAxis(function.Function):
12 
13     """Function that splits multiple arrays along the specified axis."""
14 
15     def __init__(self, indices_or_sections, axis):
16         if not isinstance(indices_or_sections, (int, collections.Iterable)):
17             raise TypeError('indices_or_sections must be integer or 1-D array')
18         self.indices_or_sections = indices_or_sections
19         self.axis = axis
20 
21     def check_type_forward(self, in_types):
22         type_check.expect(in_types.size() == 1)
23         type_check.expect(in_types[0].ndim > self.axis)
24 
25         if isinstance(self.indices_or_sections, collections.Iterable):
26             max_index = type_check.Variable(
27                 self.indices_or_sections[-1], 'max_index')
28             type_check.expect(in_types[0].shape[self.axis] > max_index)
29         else:
30             sections = type_check.Variable(
31                 self.indices_or_sections, 'sections')
32             type_check.expect(in_types[0].shape[self.axis] % sections == 0)
33 
34     def forward(self, x):
35         if isinstance(self.indices_or_sections, collections.Iterable):
36             cdimx = x[0].shape[self.axis]
37             ind = list(self.indices_or_sections)
38             ind.append(cdimx)
39             prev_i = 0
40             for i in ind:
41                 cdimy = max(0, min(i, cdimx) - prev_i)
42                 if cdimy == 0:
43                     raise ValueError('Not support if shape contains 0')
44                 prev_i = i
45         xp = cuda.get_array_module(*x)
46         return tuple(xp.split(x[0], self.indices_or_sections, self.axis))
47 
48     def backward(self, x, gys):
49         xp = cuda.get_array_module(*x)
50         if any(gy is None for gy in gys):
51             gx = xp.zeros_like(x[0])
52             gxs = xp.split(gx, self.indices_or_sections, self.axis)
53             for gxi, gy in six.moves.zip(gxs, gys):
54                 if gy is None:
55                     continue
56                 gxi[:] = gy
57             return gx,
58         else:
59             return xp.concatenate(gys, axis=self.axis),
60 
61 
62 def split_axis(x, indices_or_sections, axis, force_tuple=False):
63     """Splits given variables along an axis.
64 
65     Args:
66         x (tuple of Variables): Variables to be split.
67         indices_or_sections (int or 1-D array): If this argument is an integer,
68             N, the array will be divided into N equal arrays along axis.
69             If it is a 1-D array of sorted integers, it
70             indicates the positions where the array is split.
71         axis (int): Axis that the input array is split along.
72         force_tuple (bool): If ``True``, this method returns a tuple even when
73             the number of outputs is one.
74 
75     Returns:
76         tuple or Variable: Tuple of :class:`~chainer.Variable` objects
77              if the number of outputs is more than 1 or
78              :class:`~chainer.Variable` otherwise.
79              When ``force_tuple`` is ``True``, returned value is always a tuple
80              regardless of the number of outputs.
81 
82     .. note::
83         This function raises :class:`ValueError` if at least
84         one of the outputs is split to zero-size
85         (i.e. ``axis``-th value of its shape is zero).
86 
87     """
88     res = SplitAxis(indices_or_sections, axis)(x)
89     if force_tuple and isinstance(res, chainer.Variable):
90         res = (res,)
91     return res
92
```
--- 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/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
@@ -23,9 +23,10 @@
         type_check.expect(in_types[0].ndim > self.axis)
 
         if isinstance(self.indices_or_sections, collections.Iterable):
-            max_index = type_check.Variable(
-                self.indices_or_sections[-1], 'max_index')
-            type_check.expect(in_types[0].shape[self.axis] > max_index)
+            if len(self.indices_or_sections) > 0:
+                max_index = type_check.Variable(
+                    self.indices_or_sections[-1], 'max_index')
+                type_check.expect(in_types[0].shape[self.axis] > max_index)
         else:
             sections = type_check.Variable(
                 self.indices_or_sections, 'sections')

{"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@@ -23,9 +23,10 @@\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+            if len(self.indices_or_sections) > 0:\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", "issue": "`split_axis` doesn't support empty sections\nThis code causes a TypeError.\n`functions.split_axis(x, [], 0)`\n\n", "before_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 along 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"}], "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 along 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            if len(self.indices_or_sections) > 0:\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"}]}