We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Support ChainerX in F.GetItem backward
`GetItemGrad` does not suport it yet.

Related: #5944

--- 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/get_item.py`
Content:
```
1 import numpy
2 
3 import chainer
4 from chainer import backend
5 from chainer import function_node
6 from chainer import utils
7 from chainer.utils import type_check
8 from chainer import variable
9 import chainerx
10 
11 
12 _numpy_supports_0d_bool_index = \
13     numpy.lib.NumpyVersion(numpy.__version__) >= '1.13.0'
14 
15 
16 class GetItem(function_node.FunctionNode):
17 
18     """Function that slices array and extract elements."""
19 
20     def __init__(self, slices):
21         if isinstance(slices, list):
22             if all([isinstance(s, int) for s in slices]):
23                 slices = slices,
24             slices = tuple(slices)
25         elif not isinstance(slices, tuple):
26             slices = slices,
27 
28         if chainer.is_debug():
29             n_ellipses = 0
30             for s in slices:
31                 if s is Ellipsis:
32                     n_ellipses += 1
33             if n_ellipses > 1:
34                 raise ValueError('Only one Ellipsis is allowed')
35 
36         self.slices = slices
37 
38     def check_type_forward(self, in_types):
39         type_check._argname(in_types, ('x',))
40 
41     def forward(self, xs):
42         slices = tuple([
43             backend.from_chx(s) if isinstance(s, chainerx.ndarray) else s
44             for s in self.slices])
45         return utils.force_array(xs[0][slices]),
46 
47     def backward(self, indexes, gy):
48         return GetItemGrad(
49             self.slices, self.inputs[0].shape).apply(gy)
50 
51 
52 class GetItemGrad(function_node.FunctionNode):
53 
54     def __init__(self, slices, in_shape):
55         self.slices = slices
56         self._in_shape = in_shape
57 
58     def forward(self, inputs):
59         gy, = inputs
60         xp = backend.get_array_module(*inputs)
61         gx = xp.zeros(self._in_shape, gy.dtype)
62         if xp is numpy:
63             try:
64                 numpy.add.at(gx, self.slices, gy)
65             except IndexError:
66                 done = False
67                 # In numpy<1.13, 0-dim boolean index is not supported in
68                 # numpy.add.at and it's supported for 0-dim arr in
69                 # arr.__getitem__.
70                 if not _numpy_supports_0d_bool_index and len(self.slices) == 1:
71                     idx = numpy.asanyarray(self.slices[0])
72                     if idx.dtype == numpy.dtype(bool):
73                         # Convert the array and the mask to 1-dim.
74                         # numpy.add.at with them is supported in older numpy.
75                         numpy.add.at(gx[None], idx[None], gy)
76                         done = True
77 
78                 if not done:
79                     msg = '''
80 GetItem does not support backward for this slices. The slices argument is not
81 supported by numpy.add.at, while it is supported by numpy.ndarray.__getitem__.
82 
83 Please report this error to the issue tracker with the stack trace,
84 the information of your environment, and your script:
85 https://github.com/chainer/chainer/issues/new.
86 '''
87                     raise IndexError(msg)
88         else:
89             gx.scatter_add(self.slices, inputs[0])
90         return gx,
91 
92     def backward(self, indexes, ggx):
93         return GetItem(self.slices).apply(ggx)
94 
95 
96 def get_item(x, slices):
97     """Extract elements from array with specified shape, axes and offsets.
98 
99     Args:
100         x (:class:`~chainer.Variable` or :ref:`ndarray`):
101             A variable to be sliced.
102         slices (int, slice, Ellipsis, None, integer array-like, boolean\
103         array-like or tuple of them):
104             An object to specify the selection of elements.
105 
106     Returns:
107         A :class:`~chainer.Variable` object which contains sliced array of
108         ``x``.
109 
110     .. note::
111 
112         It only supports types that are supported by CUDA's atomicAdd when
113         an integer array is included in ``slices``.
114         The supported types are ``numpy.float32``, ``numpy.int32``,
115         ``numpy.uint32``, ``numpy.uint64`` and ``numpy.ulonglong``.
116 
117     .. note::
118 
119         It does not support ``slices`` that contains multiple boolean arrays.
120 
121     .. note::
122 
123        See NumPy documentation for details of `indexing
124        <https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html>`_.
125 
126     .. admonition:: Example
127 
128         >>> x = np.arange(12).reshape((2, 2, 3))
129         >>> x
130         array([[[ 0,  1,  2],
131                 [ 3,  4,  5]],
132         <BLANKLINE>
133                [[ 6,  7,  8],
134                 [ 9, 10, 11]]])
135         >>> F.get_item(x, 0)
136         variable([[0, 1, 2],
137                   [3, 4, 5]])
138         >>> F.get_item(x, (0, 0, slice(0, 2, 1)))  # equals x[0, 0, 0:2:1]
139         variable([0, 1])
140         >>> F.get_item(x, (Ellipsis, 2))  # equals x[..., 2]
141         variable([[ 2,  5],
142                   [ 8, 11]])
143         >>> F.get_item(x, (1, np.newaxis, 1, 0))  # equals x[1, None, 1, 0]
144         variable([9])
145 
146     """
147     return GetItem(slices).apply((x,))[0]
148 
149 
150 def install_variable_get_item():
151     variable.Variable.__getitem__ = get_item
152
```
--- 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/get_item.py b/chainer/functions/array/get_item.py
--- a/chainer/functions/array/get_item.py
+++ b/chainer/functions/array/get_item.py
@@ -56,19 +56,23 @@
         self._in_shape = in_shape
 
     def forward(self, inputs):
+        slices = tuple([
+            backend.from_chx(s) if isinstance(s, chainerx.ndarray) else s
+            for s in self.slices])
+
         gy, = inputs
         xp = backend.get_array_module(*inputs)
         gx = xp.zeros(self._in_shape, gy.dtype)
         if xp is numpy:
             try:
-                numpy.add.at(gx, self.slices, gy)
+                numpy.add.at(gx, slices, gy)
             except IndexError:
                 done = False
                 # In numpy<1.13, 0-dim boolean index is not supported in
                 # numpy.add.at and it's supported for 0-dim arr in
                 # arr.__getitem__.
-                if not _numpy_supports_0d_bool_index and len(self.slices) == 1:
-                    idx = numpy.asanyarray(self.slices[0])
+                if not _numpy_supports_0d_bool_index and len(slices) == 1:
+                    idx = numpy.asanyarray(slices[0])
                     if idx.dtype == numpy.dtype(bool):
                         # Convert the array and the mask to 1-dim.
                         # numpy.add.at with them is supported in older numpy.
@@ -86,7 +90,7 @@
 '''
                     raise IndexError(msg)
         else:
-            gx.scatter_add(self.slices, inputs[0])
+            gx.scatter_add(slices, inputs[0])
         return gx,
 
     def backward(self, indexes, ggx):

{"golden_diff": "diff --git a/chainer/functions/array/get_item.py b/chainer/functions/array/get_item.py\n--- a/chainer/functions/array/get_item.py\n+++ b/chainer/functions/array/get_item.py\n@@ -56,19 +56,23 @@\n         self._in_shape = in_shape\n \n     def forward(self, inputs):\n+        slices = tuple([\n+            backend.from_chx(s) if isinstance(s, chainerx.ndarray) else s\n+            for s in self.slices])\n+\n         gy, = inputs\n         xp = backend.get_array_module(*inputs)\n         gx = xp.zeros(self._in_shape, gy.dtype)\n         if xp is numpy:\n             try:\n-                numpy.add.at(gx, self.slices, gy)\n+                numpy.add.at(gx, slices, gy)\n             except IndexError:\n                 done = False\n                 # In numpy<1.13, 0-dim boolean index is not supported in\n                 # numpy.add.at and it's supported for 0-dim arr in\n                 # arr.__getitem__.\n-                if not _numpy_supports_0d_bool_index and len(self.slices) == 1:\n-                    idx = numpy.asanyarray(self.slices[0])\n+                if not _numpy_supports_0d_bool_index and len(slices) == 1:\n+                    idx = numpy.asanyarray(slices[0])\n                     if idx.dtype == numpy.dtype(bool):\n                         # Convert the array and the mask to 1-dim.\n                         # numpy.add.at with them is supported in older numpy.\n@@ -86,7 +90,7 @@\n '''\n                     raise IndexError(msg)\n         else:\n-            gx.scatter_add(self.slices, inputs[0])\n+            gx.scatter_add(slices, inputs[0])\n         return gx,\n \n     def backward(self, indexes, ggx):\n", "issue": "Support ChainerX in F.GetItem backward\n`GetItemGrad` does not suport it yet.\r\n\r\nRelated: #5944\n", "before_files": [{"content": "import numpy\n\nimport chainer\nfrom chainer import backend\nfrom chainer import function_node\nfrom chainer import utils\nfrom chainer.utils import type_check\nfrom chainer import variable\nimport chainerx\n\n\n_numpy_supports_0d_bool_index = \\\n    numpy.lib.NumpyVersion(numpy.__version__) >= '1.13.0'\n\n\nclass GetItem(function_node.FunctionNode):\n\n    \"\"\"Function that slices array and extract elements.\"\"\"\n\n    def __init__(self, slices):\n        if isinstance(slices, list):\n            if all([isinstance(s, int) for s in slices]):\n                slices = slices,\n            slices = tuple(slices)\n        elif not isinstance(slices, tuple):\n            slices = slices,\n\n        if chainer.is_debug():\n            n_ellipses = 0\n            for s in slices:\n                if s is Ellipsis:\n                    n_ellipses += 1\n            if n_ellipses > 1:\n                raise ValueError('Only one Ellipsis is allowed')\n\n        self.slices = slices\n\n    def check_type_forward(self, in_types):\n        type_check._argname(in_types, ('x',))\n\n    def forward(self, xs):\n        slices = tuple([\n            backend.from_chx(s) if isinstance(s, chainerx.ndarray) else s\n            for s in self.slices])\n        return utils.force_array(xs[0][slices]),\n\n    def backward(self, indexes, gy):\n        return GetItemGrad(\n            self.slices, self.inputs[0].shape).apply(gy)\n\n\nclass GetItemGrad(function_node.FunctionNode):\n\n    def __init__(self, slices, in_shape):\n        self.slices = slices\n        self._in_shape = in_shape\n\n    def forward(self, inputs):\n        gy, = inputs\n        xp = backend.get_array_module(*inputs)\n        gx = xp.zeros(self._in_shape, gy.dtype)\n        if xp is numpy:\n            try:\n                numpy.add.at(gx, self.slices, gy)\n            except IndexError:\n                done = False\n                # In numpy<1.13, 0-dim boolean index is not supported in\n                # numpy.add.at and it's supported for 0-dim arr in\n                # arr.__getitem__.\n                if not _numpy_supports_0d_bool_index and len(self.slices) == 1:\n                    idx = numpy.asanyarray(self.slices[0])\n                    if idx.dtype == numpy.dtype(bool):\n                        # Convert the array and the mask to 1-dim.\n                        # numpy.add.at with them is supported in older numpy.\n                        numpy.add.at(gx[None], idx[None], gy)\n                        done = True\n\n                if not done:\n                    msg = '''\nGetItem does not support backward for this slices. The slices argument is not\nsupported by numpy.add.at, while it is supported by numpy.ndarray.__getitem__.\n\nPlease report this error to the issue tracker with the stack trace,\nthe information of your environment, and your script:\nhttps://github.com/chainer/chainer/issues/new.\n'''\n                    raise IndexError(msg)\n        else:\n            gx.scatter_add(self.slices, inputs[0])\n        return gx,\n\n    def backward(self, indexes, ggx):\n        return GetItem(self.slices).apply(ggx)\n\n\ndef get_item(x, slices):\n    \"\"\"Extract elements from array with specified shape, axes and offsets.\n\n    Args:\n        x (:class:`~chainer.Variable` or :ref:`ndarray`):\n            A variable to be sliced.\n        slices (int, slice, Ellipsis, None, integer array-like, boolean\\\n        array-like or tuple of them):\n            An object to specify the selection of elements.\n\n    Returns:\n        A :class:`~chainer.Variable` object which contains sliced array of\n        ``x``.\n\n    .. note::\n\n        It only supports types that are supported by CUDA's atomicAdd when\n        an integer array is included in ``slices``.\n        The supported types are ``numpy.float32``, ``numpy.int32``,\n        ``numpy.uint32``, ``numpy.uint64`` and ``numpy.ulonglong``.\n\n    .. note::\n\n        It does not support ``slices`` that contains multiple boolean arrays.\n\n    .. note::\n\n       See NumPy documentation for details of `indexing\n       <https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html>`_.\n\n    .. admonition:: Example\n\n        >>> x = np.arange(12).reshape((2, 2, 3))\n        >>> x\n        array([[[ 0,  1,  2],\n                [ 3,  4,  5]],\n        <BLANKLINE>\n               [[ 6,  7,  8],\n                [ 9, 10, 11]]])\n        >>> F.get_item(x, 0)\n        variable([[0, 1, 2],\n                  [3, 4, 5]])\n        >>> F.get_item(x, (0, 0, slice(0, 2, 1)))  # equals x[0, 0, 0:2:1]\n        variable([0, 1])\n        >>> F.get_item(x, (Ellipsis, 2))  # equals x[..., 2]\n        variable([[ 2,  5],\n                  [ 8, 11]])\n        >>> F.get_item(x, (1, np.newaxis, 1, 0))  # equals x[1, None, 1, 0]\n        variable([9])\n\n    \"\"\"\n    return GetItem(slices).apply((x,))[0]\n\n\ndef install_variable_get_item():\n    variable.Variable.__getitem__ = get_item\n", "path": "chainer/functions/array/get_item.py"}], "after_files": [{"content": "import numpy\n\nimport chainer\nfrom chainer import backend\nfrom chainer import function_node\nfrom chainer import utils\nfrom chainer.utils import type_check\nfrom chainer import variable\nimport chainerx\n\n\n_numpy_supports_0d_bool_index = \\\n    numpy.lib.NumpyVersion(numpy.__version__) >= '1.13.0'\n\n\nclass GetItem(function_node.FunctionNode):\n\n    \"\"\"Function that slices array and extract elements.\"\"\"\n\n    def __init__(self, slices):\n        if isinstance(slices, list):\n            if all([isinstance(s, int) for s in slices]):\n                slices = slices,\n            slices = tuple(slices)\n        elif not isinstance(slices, tuple):\n            slices = slices,\n\n        if chainer.is_debug():\n            n_ellipses = 0\n            for s in slices:\n                if s is Ellipsis:\n                    n_ellipses += 1\n            if n_ellipses > 1:\n                raise ValueError('Only one Ellipsis is allowed')\n\n        self.slices = slices\n\n    def check_type_forward(self, in_types):\n        type_check._argname(in_types, ('x',))\n\n    def forward(self, xs):\n        slices = tuple([\n            backend.from_chx(s) if isinstance(s, chainerx.ndarray) else s\n            for s in self.slices])\n        return utils.force_array(xs[0][slices]),\n\n    def backward(self, indexes, gy):\n        return GetItemGrad(\n            self.slices, self.inputs[0].shape).apply(gy)\n\n\nclass GetItemGrad(function_node.FunctionNode):\n\n    def __init__(self, slices, in_shape):\n        self.slices = slices\n        self._in_shape = in_shape\n\n    def forward(self, inputs):\n        slices = tuple([\n            backend.from_chx(s) if isinstance(s, chainerx.ndarray) else s\n            for s in self.slices])\n\n        gy, = inputs\n        xp = backend.get_array_module(*inputs)\n        gx = xp.zeros(self._in_shape, gy.dtype)\n        if xp is numpy:\n            try:\n                numpy.add.at(gx, slices, gy)\n            except IndexError:\n                done = False\n                # In numpy<1.13, 0-dim boolean index is not supported in\n                # numpy.add.at and it's supported for 0-dim arr in\n                # arr.__getitem__.\n                if not _numpy_supports_0d_bool_index and len(slices) == 1:\n                    idx = numpy.asanyarray(slices[0])\n                    if idx.dtype == numpy.dtype(bool):\n                        # Convert the array and the mask to 1-dim.\n                        # numpy.add.at with them is supported in older numpy.\n                        numpy.add.at(gx[None], idx[None], gy)\n                        done = True\n\n                if not done:\n                    msg = '''\nGetItem does not support backward for this slices. The slices argument is not\nsupported by numpy.add.at, while it is supported by numpy.ndarray.__getitem__.\n\nPlease report this error to the issue tracker with the stack trace,\nthe information of your environment, and your script:\nhttps://github.com/chainer/chainer/issues/new.\n'''\n                    raise IndexError(msg)\n        else:\n            gx.scatter_add(slices, inputs[0])\n        return gx,\n\n    def backward(self, indexes, ggx):\n        return GetItem(self.slices).apply(ggx)\n\n\ndef get_item(x, slices):\n    \"\"\"Extract elements from array with specified shape, axes and offsets.\n\n    Args:\n        x (:class:`~chainer.Variable` or :ref:`ndarray`):\n            A variable to be sliced.\n        slices (int, slice, Ellipsis, None, integer array-like, boolean\\\n        array-like or tuple of them):\n            An object to specify the selection of elements.\n\n    Returns:\n        A :class:`~chainer.Variable` object which contains sliced array of\n        ``x``.\n\n    .. note::\n\n        It only supports types that are supported by CUDA's atomicAdd when\n        an integer array is included in ``slices``.\n        The supported types are ``numpy.float32``, ``numpy.int32``,\n        ``numpy.uint32``, ``numpy.uint64`` and ``numpy.ulonglong``.\n\n    .. note::\n\n        It does not support ``slices`` that contains multiple boolean arrays.\n\n    .. note::\n\n       See NumPy documentation for details of `indexing\n       <https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html>`_.\n\n    .. admonition:: Example\n\n        >>> x = np.arange(12).reshape((2, 2, 3))\n        >>> x\n        array([[[ 0,  1,  2],\n                [ 3,  4,  5]],\n        <BLANKLINE>\n               [[ 6,  7,  8],\n                [ 9, 10, 11]]])\n        >>> F.get_item(x, 0)\n        variable([[0, 1, 2],\n                  [3, 4, 5]])\n        >>> F.get_item(x, (0, 0, slice(0, 2, 1)))  # equals x[0, 0, 0:2:1]\n        variable([0, 1])\n        >>> F.get_item(x, (Ellipsis, 2))  # equals x[..., 2]\n        variable([[ 2,  5],\n                  [ 8, 11]])\n        >>> F.get_item(x, (1, np.newaxis, 1, 0))  # equals x[1, None, 1, 0]\n        variable([9])\n\n    \"\"\"\n    return GetItem(slices).apply((x,))[0]\n\n\ndef install_variable_get_item():\n    variable.Variable.__getitem__ = get_item\n", "path": "chainer/functions/array/get_item.py"}]}