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()

```

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()

{"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"}]}