We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Make cupy.sort support arrays with rank two or more.
# Background
Arrays sorted with `cupy.sort` operation have some properties such as dtype, rank, sorting axis and C/F-contiguousness. Currently, `cupy.sort` supports sorting arrays only with the rank of one because of its implementation reason, see #55.

# Problem
This issue addresses a problem that makes `cupy.sort` support sorting arrays with the rank of two or more, with the last axis and C-contiguousness.

# Approach

**Rank two**

For an array with the rank of two, 

```
[[4, 3]
 [2, 1]]
```

treating the array as flattened one, `[4, 3, 2 ,1]`, and providing the following comparator in pseudo code to underlying Thrust library:

```
if floor(i / 2) < floor(j / 2) then return true;
else if floor(i / 2) > floor(j / 2) then return false;
else return data[i] < data[j];
```

where `i` and `j` are array indices, and `data[i]` represents `i` th element of array `data`,

we get the C-contiguous array sorted with the last axis.

```
[[3, 4]
 [1, 2]]
```

**Rank N**

Generalized to the rank of N with shape `(d_0, d_1, ..., d_n-1)`, the following comparator works:

```
if floor(i / d_n-1) < floor(j / d_n-1) then return true;
else if floor(i / d_n-1) > floor(j / d_n-1) then return false;
else return data[i] < data[j];
```


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `cupy/sorting/sort.py`
Content:
```
1 import cupy
2 import numpy
3 
4 if cupy.cuda.thrust_enabled:
5     from cupy.cuda import thrust
6 
7 
8 def sort(a):
9     """Returns a sorted copy of an array with a stable sorting algorithm.
10 
11     Args:
12         a (cupy.ndarray): Array to be sorted.
13 
14     Returns:
15         cupy.ndarray: Array of the same type and shape as ``a``.
16 
17     .. note::
18        For its implementation reason, ``cupy.sort`` currently supports only
19        arrays with their rank of one and does not support ``axis``, ``kind``
20        and ``order`` parameters that ``numpy.sort`` does support.
21 
22     .. seealso:: :func:`numpy.sort`
23 
24     """
25     ret = a.copy()
26     ret.sort()
27     return ret
28 
29 
30 def lexsort(keys):
31     """Perform an indirect sort using an array of keys.
32 
33     Args:
34         keys (cupy.ndarray): ``(k, N)`` array containing ``k`` ``(N,)``-shaped
35             arrays. The ``k`` different "rows" to be sorted. The last row is
36             the primary sort key.
37 
38     Returns:
39         cupy.ndarray: Array of indices that sort the keys.
40 
41     .. note::
42         For its implementation reason, ``cupy.lexsort`` currently supports only
43         keys with their rank of one or two and does not support ``axis``
44         parameter that ``numpy.lexsort`` supports.
45 
46     .. seealso:: :func:`numpy.lexsort`
47 
48     """
49 
50     # TODO(takagi): Support axis argument.
51 
52     if not cupy.cuda.thrust_enabled:
53         raise RuntimeError('Thrust is needed to use cupy.lexsort. Please '
54                            'install CUDA Toolkit with Thrust then reinstall '
55                            'CuPy after uninstalling it.')
56 
57     if keys.ndim == ():
58         # as numpy.lexsort() raises
59         raise TypeError('need sequence of keys with len > 0 in lexsort')
60 
61     if keys.ndim == 1:
62         return 0
63 
64     # TODO(takagi): Support ranks of three or more.
65     if keys.ndim > 2:
66         raise NotImplementedError('Keys with the rank of three or more is not '
67                                   'supported in lexsort')
68 
69     idx_array = cupy.ndarray(keys._shape[1:], dtype=numpy.intp)
70     k = keys._shape[0]
71     n = keys._shape[1]
72     thrust.lexsort(keys.dtype, idx_array.data.ptr, keys.data.ptr, k, n)
73 
74     return idx_array
75 
76 
77 def argsort(a):
78     """Return the indices that would sort an array with a stable sorting.
79 
80     Args:
81         a (cupy.ndarray): Array to sort.
82 
83     Returns:
84         cupy.ndarray: Array of indices that sort ``a``.
85 
86     .. note::
87        For its implementation reason, ``cupy.argsort`` currently supports only
88        arrays with their rank of one and does not support ``axis``, ``kind``
89        and ``order`` parameters that ``numpy.argsort`` supports.
90 
91     .. seealso:: :func:`numpy.argsort`
92 
93     """
94     return a.argsort()
95 
96 
97 # TODO(okuta): Implement msort
98 
99 
100 # TODO(okuta): Implement sort_complex
101 
102 
103 # TODO(okuta): Implement partition
104 
105 
106 # TODO(okuta): Implement argpartition
107
```
--- 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/cupy/sorting/sort.py b/cupy/sorting/sort.py
--- a/cupy/sorting/sort.py
+++ b/cupy/sorting/sort.py
@@ -15,9 +15,9 @@
         cupy.ndarray: Array of the same type and shape as ``a``.
 
     .. note::
-       For its implementation reason, ``cupy.sort`` currently supports only
-       arrays with their rank of one and does not support ``axis``, ``kind``
-       and ``order`` parameters that ``numpy.sort`` does support.
+       For its implementation reason, ``cupy.sort`` currently does not support
+       ``axis``, ``kind`` and ``order`` parameters that ``numpy.sort`` does
+       support.
 
     .. seealso:: :func:`numpy.sort`

{"golden_diff": "diff --git a/cupy/sorting/sort.py b/cupy/sorting/sort.py\n--- a/cupy/sorting/sort.py\n+++ b/cupy/sorting/sort.py\n@@ -15,9 +15,9 @@\n         cupy.ndarray: Array of the same type and shape as ``a``.\n \n     .. note::\n-       For its implementation reason, ``cupy.sort`` currently supports only\n-       arrays with their rank of one and does not support ``axis``, ``kind``\n-       and ``order`` parameters that ``numpy.sort`` does support.\n+       For its implementation reason, ``cupy.sort`` currently does not support\n+       ``axis``, ``kind`` and ``order`` parameters that ``numpy.sort`` does\n+       support.\n \n     .. seealso:: :func:`numpy.sort`\n", "issue": "Make cupy.sort support arrays with rank two or more.\n# Background\r\nArrays sorted with `cupy.sort` operation have some properties such as dtype, rank, sorting axis and C/F-contiguousness. Currently, `cupy.sort` supports sorting arrays only with the rank of one because of its implementation reason, see #55.\r\n\r\n# Problem\r\nThis issue addresses a problem that makes `cupy.sort` support sorting arrays with the rank of two or more, with the last axis and C-contiguousness.\r\n\r\n# Approach\r\n\r\n**Rank two**\r\n\r\nFor an array with the rank of two, \r\n\r\n```\r\n[[4, 3]\r\n [2, 1]]\r\n```\r\n\r\ntreating the array as flattened one, `[4, 3, 2 ,1]`, and providing the following comparator in pseudo code to underlying Thrust library:\r\n\r\n```\r\nif floor(i / 2) < floor(j / 2) then return true;\r\nelse if floor(i / 2) > floor(j / 2) then return false;\r\nelse return data[i] < data[j];\r\n```\r\n\r\nwhere `i` and `j` are array indices, and `data[i]` represents `i` th element of array `data`,\r\n\r\nwe get the C-contiguous array sorted with the last axis.\r\n\r\n```\r\n[[3, 4]\r\n [1, 2]]\r\n```\r\n\r\n**Rank N**\r\n\r\nGeneralized to the rank of N with shape `(d_0, d_1, ..., d_n-1)`, the following comparator works:\r\n\r\n```\r\nif floor(i / d_n-1) < floor(j / d_n-1) then return true;\r\nelse if floor(i / d_n-1) > floor(j / d_n-1) then return false;\r\nelse return data[i] < data[j];\r\n```\r\n\n", "before_files": [{"content": "import cupy\nimport numpy\n\nif cupy.cuda.thrust_enabled:\n    from cupy.cuda import thrust\n\n\ndef sort(a):\n    \"\"\"Returns a sorted copy of an array with a stable sorting algorithm.\n\n    Args:\n        a (cupy.ndarray): Array to be sorted.\n\n    Returns:\n        cupy.ndarray: Array of the same type and shape as ``a``.\n\n    .. note::\n       For its implementation reason, ``cupy.sort`` currently supports only\n       arrays with their rank of one and does not support ``axis``, ``kind``\n       and ``order`` parameters that ``numpy.sort`` does support.\n\n    .. seealso:: :func:`numpy.sort`\n\n    \"\"\"\n    ret = a.copy()\n    ret.sort()\n    return ret\n\n\ndef lexsort(keys):\n    \"\"\"Perform an indirect sort using an array of keys.\n\n    Args:\n        keys (cupy.ndarray): ``(k, N)`` array containing ``k`` ``(N,)``-shaped\n            arrays. The ``k`` different \"rows\" to be sorted. The last row is\n            the primary sort key.\n\n    Returns:\n        cupy.ndarray: Array of indices that sort the keys.\n\n    .. note::\n        For its implementation reason, ``cupy.lexsort`` currently supports only\n        keys with their rank of one or two and does not support ``axis``\n        parameter that ``numpy.lexsort`` supports.\n\n    .. seealso:: :func:`numpy.lexsort`\n\n    \"\"\"\n\n    # TODO(takagi): Support axis argument.\n\n    if not cupy.cuda.thrust_enabled:\n        raise RuntimeError('Thrust is needed to use cupy.lexsort. Please '\n                           'install CUDA Toolkit with Thrust then reinstall '\n                           'CuPy after uninstalling it.')\n\n    if keys.ndim == ():\n        # as numpy.lexsort() raises\n        raise TypeError('need sequence of keys with len > 0 in lexsort')\n\n    if keys.ndim == 1:\n        return 0\n\n    # TODO(takagi): Support ranks of three or more.\n    if keys.ndim > 2:\n        raise NotImplementedError('Keys with the rank of three or more is not '\n                                  'supported in lexsort')\n\n    idx_array = cupy.ndarray(keys._shape[1:], dtype=numpy.intp)\n    k = keys._shape[0]\n    n = keys._shape[1]\n    thrust.lexsort(keys.dtype, idx_array.data.ptr, keys.data.ptr, k, n)\n\n    return idx_array\n\n\ndef argsort(a):\n    \"\"\"Return the indices that would sort an array with a stable sorting.\n\n    Args:\n        a (cupy.ndarray): Array to sort.\n\n    Returns:\n        cupy.ndarray: Array of indices that sort ``a``.\n\n    .. note::\n       For its implementation reason, ``cupy.argsort`` currently supports only\n       arrays with their rank of one and does not support ``axis``, ``kind``\n       and ``order`` parameters that ``numpy.argsort`` supports.\n\n    .. seealso:: :func:`numpy.argsort`\n\n    \"\"\"\n    return a.argsort()\n\n\n# TODO(okuta): Implement msort\n\n\n# TODO(okuta): Implement sort_complex\n\n\n# TODO(okuta): Implement partition\n\n\n# TODO(okuta): Implement argpartition\n", "path": "cupy/sorting/sort.py"}], "after_files": [{"content": "import cupy\nimport numpy\n\nif cupy.cuda.thrust_enabled:\n    from cupy.cuda import thrust\n\n\ndef sort(a):\n    \"\"\"Returns a sorted copy of an array with a stable sorting algorithm.\n\n    Args:\n        a (cupy.ndarray): Array to be sorted.\n\n    Returns:\n        cupy.ndarray: Array of the same type and shape as ``a``.\n\n    .. note::\n       For its implementation reason, ``cupy.sort`` currently does not support\n       ``axis``, ``kind`` and ``order`` parameters that ``numpy.sort`` does\n       support.\n\n    .. seealso:: :func:`numpy.sort`\n\n    \"\"\"\n    ret = a.copy()\n    ret.sort()\n    return ret\n\n\ndef lexsort(keys):\n    \"\"\"Perform an indirect sort using an array of keys.\n\n    Args:\n        keys (cupy.ndarray): ``(k, N)`` array containing ``k`` ``(N,)``-shaped\n            arrays. The ``k`` different \"rows\" to be sorted. The last row is\n            the primary sort key.\n\n    Returns:\n        cupy.ndarray: Array of indices that sort the keys.\n\n    .. note::\n        For its implementation reason, ``cupy.lexsort`` currently supports only\n        keys with their rank of one or two and does not support ``axis``\n        parameter that ``numpy.lexsort`` supports.\n\n    .. seealso:: :func:`numpy.lexsort`\n\n    \"\"\"\n\n    # TODO(takagi): Support axis argument.\n\n    if not cupy.cuda.thrust_enabled:\n        raise RuntimeError('Thrust is needed to use cupy.lexsort. Please '\n                           'install CUDA Toolkit with Thrust then reinstall '\n                           'CuPy after uninstalling it.')\n\n    if keys.ndim == ():\n        # as numpy.lexsort() raises\n        raise TypeError('need sequence of keys with len > 0 in lexsort')\n\n    if keys.ndim == 1:\n        return 0\n\n    # TODO(takagi): Support ranks of three or more.\n    if keys.ndim > 2:\n        raise NotImplementedError('Keys with the rank of three or more is not '\n                                  'supported in lexsort')\n\n    idx_array = cupy.ndarray(keys._shape[1:], dtype=numpy.intp)\n    k = keys._shape[0]\n    n = keys._shape[1]\n    thrust.lexsort(keys.dtype, idx_array.data.ptr, keys.data.ptr, k, n)\n\n    return idx_array\n\n\ndef argsort(a):\n    \"\"\"Return the indices that would sort an array with a stable sorting.\n\n    Args:\n        a (cupy.ndarray): Array to sort.\n\n    Returns:\n        cupy.ndarray: Array of indices that sort ``a``.\n\n    .. note::\n       For its implementation reason, ``cupy.argsort`` currently supports only\n       arrays with their rank of one and does not support ``axis``, ``kind``\n       and ``order`` parameters that ``numpy.argsort`` supports.\n\n    .. seealso:: :func:`numpy.argsort`\n\n    \"\"\"\n    return a.argsort()\n\n\n# TODO(okuta): Implement msort\n\n\n# TODO(okuta): Implement sort_complex\n\n\n# TODO(okuta): Implement partition\n\n\n# TODO(okuta): Implement argpartition\n", "path": "cupy/sorting/sort.py"}]}