prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  Support ellipsis in `Array::At` and `__getitem__`
  Depends on #7559 because `py::ellipsis` is supported from v2.3.0.
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `chainerx/_fallback_workarounds.py`
  Content:
  ```
  1 # This file defines workaround implementation for
  2 # NumPy-compatibility functions that fall back to NumPy/CuPy functions
  3 # for native/cuda devices respecitvely.
  4 # The workaround does not support backprop, and also requires external
  5 # libraries mentioned above.
  6 # Functions defined in this file should be considered to have high priority for
  7 # genuine implementations.
  8 import numpy
  9 
  10 import chainerx
  11 
  12 
  13 try:
  14     import cupy
  15 except Exception:
  16     cupy = None
  17 
  18 
  19 class _DummyContext:
  20     def __enter__(self):
  21         pass
  22 
  23     def __exit__(self, type, value, traceback):
  24         pass
  25 
  26 
  27 _dummy_context = _DummyContext()
  28 
  29 
  30 def _to_numpy(array):
  31     assert isinstance(array, chainerx.ndarray)
  32     return chainerx.to_numpy(array, copy=False)
  33 
  34 
  35 def _from_numpy(array):
  36     assert isinstance(array, numpy.ndarray)
  37     return chainerx.array(array, copy=False)
  38 
  39 
  40 def _to_cupy(array):
  41     assert cupy is not None
  42     # Convert to cupy.ndarray on the same device as source array
  43     return chainerx._to_cupy(array)
  44 
  45 
  46 def _from_cupy(array):
  47     assert cupy is not None
  48     assert isinstance(array, cupy.ndarray)
  49     device = chainerx.get_device('cuda', array.device.id)
  50     return chainerx._core._fromrawpointer(
  51         array.data.mem.ptr,
  52         array.shape,
  53         array.dtype,
  54         array.strides,
  55         device,
  56         array.data.ptr - array.data.mem.ptr,
  57         array)
  58 
  59 
  60 def _from_chx(array, check_backprop=True):
  61     # Converts chainerx.ndarray to numpy/cupy.ndarray.
  62     # Objects with other types are kept intact.
  63     # Returns a pair: (xp, cupy device or dummy context, numpy/cupy.ndarray).
  64     if not isinstance(array, chainerx.ndarray):
  65         if (isinstance(array, numpy.ndarray)
  66                 or (cupy and isinstance(array, cupy.ndarray))):
  67             raise TypeError(
  68                 'ChainerX function fallback using NumPy/CuPy arrays '
  69                 'is not supported.')
  70         # _from_chx is also called for slice and tuple objects
  71         # Used to index a chx array
  72         return None, _dummy_context, array
  73     if check_backprop and array.is_backprop_required():
  74         raise RuntimeError(
  75             'ChainerX function fallback using NumPy/CuPy is not '
  76             'supported for arrays that are connected to a graph.')
  77     backend_name = array.device.backend.name
  78     if backend_name == 'native':
  79         return numpy, _dummy_context, _to_numpy(array)
  80     if backend_name == 'cuda':
  81         if cupy is None:
  82             raise RuntimeError(
  83                 'ChainerX fallback implementation for cuda backend requires '
  84                 'cupy to be installed.')
  85         array_cupy = _to_cupy(array)
  86         return cupy, array_cupy.device, array_cupy
  87     raise RuntimeError(
  88         'ChainerX fallback implementation only supports native or cuda '
  89         'backends.')
  90 
  91 
  92 def _to_chx(array):
  93     # Converts numpy/cupy.ndarray to chainerx.ndarray.
  94     # Objects with other types are kept intact.
  95     if isinstance(array, numpy.ndarray):
  96         return _from_numpy(array)
  97     elif cupy is not None and isinstance(array, cupy.ndarray):
  98         return _from_cupy(array)
  99     return array
  100 
  101 
  102 def _populate_module_functions():
  103 
  104     def _fix(arr):
  105         xp, dev, arr = _from_chx(arr)
  106         with dev:
  107             ret = xp.fix(arr)
  108             ret = xp.asarray(ret)
  109         return _to_chx(ret)
  110 
  111     chainerx.fix = _fix
  112 
  113 
  114 def _populate_ndarray():
  115     ndarray = chainerx.ndarray
  116 
  117     # __getitem__ with advanced indexing
  118     old_getitem = ndarray.__getitem__
  119 
  120     def __getitem__(arr, key):
  121         try:
  122             return old_getitem(arr, key)
  123         except (IndexError, chainerx.DimensionError):
  124             pass
  125 
  126         is_backprop_required = arr.is_backprop_required()
  127 
  128         xp, dev, arr = _from_chx(arr, check_backprop=False)
  129         # The elements used for indexing the array might be
  130         # also ChainerX arrays. _from_chx ignores
  131         # other types and return them as-is
  132         if isinstance(key, tuple):
  133             key = tuple([_from_chx(k, check_backprop=False)[2] for k in key])
  134         else:
  135             _, _, key = _from_chx(key, check_backprop=False)
  136 
  137         with dev:
  138             ret = arr[key]
  139 
  140         # Doing this check after the fallback __getitem__ because the error
  141         # which caused the fallback might not be due to advanced indexing.
  142         # In such case the fallback __getitem__ should also raise the error.
  143 
  144         if is_backprop_required:
  145             raise RuntimeError(
  146                 'ChainerX getitem fallback for advanced indexing is not '
  147                 'supported for arrays that are connected to a graph.')
  148 
  149         return _to_chx(ret)
  150 
  151     # __setitem__ with advanced indexing
  152     def __setitem__(self, key, value):
  153         if self.is_backprop_required():
  154             raise RuntimeError(
  155                 'ChainerX setitem fallback for advanced indexing is not '
  156                 'supported for arrays that are connected to a graph.')
  157 
  158         xp, dev, self = _from_chx(self)
  159         if isinstance(key, tuple):
  160             key = tuple([_from_chx(k)[2] for k in key])
  161         else:
  162             _, _, key = _from_chx(key)
  163         _, _, value = _from_chx(value)
  164 
  165         with dev:
  166             self[key] = value
  167 
  168     ndarray.__setitem__ = __setitem__
  169     ndarray.__getitem__ = __getitem__
  170 
  171     def tolist(arr):
  172         _, dev, arr = _from_chx(arr)
  173         with dev:
  174             ret = arr.tolist()
  175         return ret
  176 
  177     ndarray.tolist = tolist
  178 
  179 
  180 def populate():
  181     _populate_module_functions()
  182     _populate_ndarray()
  183
  ```
  --- 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/chainerx/_fallback_workarounds.py b/chainerx/_fallback_workarounds.py
  --- a/chainerx/_fallback_workarounds.py
  +++ b/chainerx/_fallback_workarounds.py
  @@ -118,10 +118,8 @@
       old_getitem = ndarray.__getitem__
   
       def __getitem__(arr, key):
  -        try:
  +        if not isinstance(key, chainerx.ndarray):
               return old_getitem(arr, key)
  -        except (IndexError, chainerx.DimensionError):
  -            pass
   
           is_backprop_required = arr.is_backprop_required()
  
verification_info: {"golden_diff": "diff --git a/chainerx/_fallback_workarounds.py b/chainerx/_fallback_workarounds.py\n--- a/chainerx/_fallback_workarounds.py\n+++ b/chainerx/_fallback_workarounds.py\n@@ -118,10 +118,8 @@\n     old_getitem = ndarray.__getitem__\n \n     def __getitem__(arr, key):\n-        try:\n+        if not isinstance(key, chainerx.ndarray):\n             return old_getitem(arr, key)\n-        except (IndexError, chainerx.DimensionError):\n-            pass\n \n         is_backprop_required = arr.is_backprop_required()\n", "issue": "Support ellipsis in `Array::At` and `__getitem__`\nDepends on #7559 because `py::ellipsis` is supported from v2.3.0.\n", "before_files": [{"content": "# This file defines workaround implementation for\n# NumPy-compatibility functions that fall back to NumPy/CuPy functions\n# for native/cuda devices respecitvely.\n# The workaround does not support backprop, and also requires external\n# libraries mentioned above.\n# Functions defined in this file should be considered to have high priority for\n# genuine implementations.\nimport numpy\n\nimport chainerx\n\n\ntry:\n    import cupy\nexcept Exception:\n    cupy = None\n\n\nclass _DummyContext:\n    def __enter__(self):\n        pass\n\n    def __exit__(self, type, value, traceback):\n        pass\n\n\n_dummy_context = _DummyContext()\n\n\ndef _to_numpy(array):\n    assert isinstance(array, chainerx.ndarray)\n    return chainerx.to_numpy(array, copy=False)\n\n\ndef _from_numpy(array):\n    assert isinstance(array, numpy.ndarray)\n    return chainerx.array(array, copy=False)\n\n\ndef _to_cupy(array):\n    assert cupy is not None\n    # Convert to cupy.ndarray on the same device as source array\n    return chainerx._to_cupy(array)\n\n\ndef _from_cupy(array):\n    assert cupy is not None\n    assert isinstance(array, cupy.ndarray)\n    device = chainerx.get_device('cuda', array.device.id)\n    return chainerx._core._fromrawpointer(\n        array.data.mem.ptr,\n        array.shape,\n        array.dtype,\n        array.strides,\n        device,\n        array.data.ptr - array.data.mem.ptr,\n        array)\n\n\ndef _from_chx(array, check_backprop=True):\n    # Converts chainerx.ndarray to numpy/cupy.ndarray.\n    # Objects with other types are kept intact.\n    # Returns a pair: (xp, cupy device or dummy context, numpy/cupy.ndarray).\n    if not isinstance(array, chainerx.ndarray):\n        if (isinstance(array, numpy.ndarray)\n                or (cupy and isinstance(array, cupy.ndarray))):\n            raise TypeError(\n                'ChainerX function fallback using NumPy/CuPy arrays '\n                'is not supported.')\n        # _from_chx is also called for slice and tuple objects\n        # Used to index a chx array\n        return None, _dummy_context, array\n    if check_backprop and array.is_backprop_required():\n        raise RuntimeError(\n            'ChainerX function fallback using NumPy/CuPy is not '\n            'supported for arrays that are connected to a graph.')\n    backend_name = array.device.backend.name\n    if backend_name == 'native':\n        return numpy, _dummy_context, _to_numpy(array)\n    if backend_name == 'cuda':\n        if cupy is None:\n            raise RuntimeError(\n                'ChainerX fallback implementation for cuda backend requires '\n                'cupy to be installed.')\n        array_cupy = _to_cupy(array)\n        return cupy, array_cupy.device, array_cupy\n    raise RuntimeError(\n        'ChainerX fallback implementation only supports native or cuda '\n        'backends.')\n\n\ndef _to_chx(array):\n    # Converts numpy/cupy.ndarray to chainerx.ndarray.\n    # Objects with other types are kept intact.\n    if isinstance(array, numpy.ndarray):\n        return _from_numpy(array)\n    elif cupy is not None and isinstance(array, cupy.ndarray):\n        return _from_cupy(array)\n    return array\n\n\ndef _populate_module_functions():\n\n    def _fix(arr):\n        xp, dev, arr = _from_chx(arr)\n        with dev:\n            ret = xp.fix(arr)\n            ret = xp.asarray(ret)\n        return _to_chx(ret)\n\n    chainerx.fix = _fix\n\n\ndef _populate_ndarray():\n    ndarray = chainerx.ndarray\n\n    # __getitem__ with advanced indexing\n    old_getitem = ndarray.__getitem__\n\n    def __getitem__(arr, key):\n        try:\n            return old_getitem(arr, key)\n        except (IndexError, chainerx.DimensionError):\n            pass\n\n        is_backprop_required = arr.is_backprop_required()\n\n        xp, dev, arr = _from_chx(arr, check_backprop=False)\n        # The elements used for indexing the array might be\n        # also ChainerX arrays. _from_chx ignores\n        # other types and return them as-is\n        if isinstance(key, tuple):\n            key = tuple([_from_chx(k, check_backprop=False)[2] for k in key])\n        else:\n            _, _, key = _from_chx(key, check_backprop=False)\n\n        with dev:\n            ret = arr[key]\n\n        # Doing this check after the fallback __getitem__ because the error\n        # which caused the fallback might not be due to advanced indexing.\n        # In such case the fallback __getitem__ should also raise the error.\n\n        if is_backprop_required:\n            raise RuntimeError(\n                'ChainerX getitem fallback for advanced indexing is not '\n                'supported for arrays that are connected to a graph.')\n\n        return _to_chx(ret)\n\n    # __setitem__ with advanced indexing\n    def __setitem__(self, key, value):\n        if self.is_backprop_required():\n            raise RuntimeError(\n                'ChainerX setitem fallback for advanced indexing is not '\n                'supported for arrays that are connected to a graph.')\n\n        xp, dev, self = _from_chx(self)\n        if isinstance(key, tuple):\n            key = tuple([_from_chx(k)[2] for k in key])\n        else:\n            _, _, key = _from_chx(key)\n        _, _, value = _from_chx(value)\n\n        with dev:\n            self[key] = value\n\n    ndarray.__setitem__ = __setitem__\n    ndarray.__getitem__ = __getitem__\n\n    def tolist(arr):\n        _, dev, arr = _from_chx(arr)\n        with dev:\n            ret = arr.tolist()\n        return ret\n\n    ndarray.tolist = tolist\n\n\ndef populate():\n    _populate_module_functions()\n    _populate_ndarray()\n", "path": "chainerx/_fallback_workarounds.py"}], "after_files": [{"content": "# This file defines workaround implementation for\n# NumPy-compatibility functions that fall back to NumPy/CuPy functions\n# for native/cuda devices respecitvely.\n# The workaround does not support backprop, and also requires external\n# libraries mentioned above.\n# Functions defined in this file should be considered to have high priority for\n# genuine implementations.\nimport numpy\n\nimport chainerx\n\n\ntry:\n    import cupy\nexcept Exception:\n    cupy = None\n\n\nclass _DummyContext:\n    def __enter__(self):\n        pass\n\n    def __exit__(self, type, value, traceback):\n        pass\n\n\n_dummy_context = _DummyContext()\n\n\ndef _to_numpy(array):\n    assert isinstance(array, chainerx.ndarray)\n    return chainerx.to_numpy(array, copy=False)\n\n\ndef _from_numpy(array):\n    assert isinstance(array, numpy.ndarray)\n    return chainerx.array(array, copy=False)\n\n\ndef _to_cupy(array):\n    assert cupy is not None\n    # Convert to cupy.ndarray on the same device as source array\n    return chainerx._to_cupy(array)\n\n\ndef _from_cupy(array):\n    assert cupy is not None\n    assert isinstance(array, cupy.ndarray)\n    device = chainerx.get_device('cuda', array.device.id)\n    return chainerx._core._fromrawpointer(\n        array.data.mem.ptr,\n        array.shape,\n        array.dtype,\n        array.strides,\n        device,\n        array.data.ptr - array.data.mem.ptr,\n        array)\n\n\ndef _from_chx(array, check_backprop=True):\n    # Converts chainerx.ndarray to numpy/cupy.ndarray.\n    # Objects with other types are kept intact.\n    # Returns a pair: (xp, cupy device or dummy context, numpy/cupy.ndarray).\n    if not isinstance(array, chainerx.ndarray):\n        if (isinstance(array, numpy.ndarray)\n                or (cupy and isinstance(array, cupy.ndarray))):\n            raise TypeError(\n                'ChainerX function fallback using NumPy/CuPy arrays '\n                'is not supported.')\n        # _from_chx is also called for slice and tuple objects\n        # Used to index a chx array\n        return None, _dummy_context, array\n    if check_backprop and array.is_backprop_required():\n        raise RuntimeError(\n            'ChainerX function fallback using NumPy/CuPy is not '\n            'supported for arrays that are connected to a graph.')\n    backend_name = array.device.backend.name\n    if backend_name == 'native':\n        return numpy, _dummy_context, _to_numpy(array)\n    if backend_name == 'cuda':\n        if cupy is None:\n            raise RuntimeError(\n                'ChainerX fallback implementation for cuda backend requires '\n                'cupy to be installed.')\n        array_cupy = _to_cupy(array)\n        return cupy, array_cupy.device, array_cupy\n    raise RuntimeError(\n        'ChainerX fallback implementation only supports native or cuda '\n        'backends.')\n\n\ndef _to_chx(array):\n    # Converts numpy/cupy.ndarray to chainerx.ndarray.\n    # Objects with other types are kept intact.\n    if isinstance(array, numpy.ndarray):\n        return _from_numpy(array)\n    elif cupy is not None and isinstance(array, cupy.ndarray):\n        return _from_cupy(array)\n    return array\n\n\ndef _populate_module_functions():\n\n    def _fix(arr):\n        xp, dev, arr = _from_chx(arr)\n        with dev:\n            ret = xp.fix(arr)\n            ret = xp.asarray(ret)\n        return _to_chx(ret)\n\n    chainerx.fix = _fix\n\n\ndef _populate_ndarray():\n    ndarray = chainerx.ndarray\n\n    # __getitem__ with advanced indexing\n    old_getitem = ndarray.__getitem__\n\n    def __getitem__(arr, key):\n        if not isinstance(key, chainerx.ndarray):\n            return old_getitem(arr, key)\n\n        is_backprop_required = arr.is_backprop_required()\n\n        xp, dev, arr = _from_chx(arr, check_backprop=False)\n        # The elements used for indexing the array might be\n        # also ChainerX arrays. _from_chx ignores\n        # other types and return them as-is\n        if isinstance(key, tuple):\n            key = tuple([_from_chx(k, check_backprop=False)[2] for k in key])\n        else:\n            _, _, key = _from_chx(key, check_backprop=False)\n\n        with dev:\n            ret = arr[key]\n\n        # Doing this check after the fallback __getitem__ because the error\n        # which caused the fallback might not be due to advanced indexing.\n        # In such case the fallback __getitem__ should also raise the error.\n\n        if is_backprop_required:\n            raise RuntimeError(\n                'ChainerX getitem fallback for advanced indexing is not '\n                'supported for arrays that are connected to a graph.')\n\n        return _to_chx(ret)\n\n    # __setitem__ with advanced indexing\n    def __setitem__(self, key, value):\n        if self.is_backprop_required():\n            raise RuntimeError(\n                'ChainerX setitem fallback for advanced indexing is not '\n                'supported for arrays that are connected to a graph.')\n\n        xp, dev, self = _from_chx(self)\n        if isinstance(key, tuple):\n            key = tuple([_from_chx(k)[2] for k in key])\n        else:\n            _, _, key = _from_chx(key)\n        _, _, value = _from_chx(value)\n\n        with dev:\n            self[key] = value\n\n    ndarray.__setitem__ = __setitem__\n    ndarray.__getitem__ = __getitem__\n\n    def tolist(arr):\n        _, dev, arr = _from_chx(arr)\n        with dev:\n            ret = arr.tolist()\n        return ret\n\n    ndarray.tolist = tolist\n\n\ndef populate():\n    _populate_module_functions()\n    _populate_ndarray()\n", "path": "chainerx/_fallback_workarounds.py"}]}