prompt: 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()
  
  ```
  
golden_diff: 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):
  
verification_info: {"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"}]}