We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
cupy.apply_along_axis failed with cupy.nonzero
### Description

cp.apply_along_axis(cp.nonzero, 1, cp.array([[1,2],[2,3]])) - failed with error

*** AttributeError: 'tuple' object has no attribute 'shape'

np.apply_along_axis(np.nonzero, 1, np.array([[1,2],[2,3]])) - is OK

UPDATE. Problem in _shape_base.py. 
line 53:
buff = cupy.empty(inarr_view.shape[:-1] + res.shape, res.dtype)

res - is tuple (as a result of cp.nonzero(1d array) of single cupy-array, so line 44 ( if cupy.isscalar(res):) doesnt convert it from tuple to cupy-array

as a temporal solution is possible to use "buffer-like" function
def cupy_nonzero (a):
    return cp.nonzero(a)[0]

### To Reproduce

```py
cp.apply_along_axis(cp.nonzero, 1, cp.array([[1,2],[2,3]]))
```


### Installation

_No response_

### Environment

```
# Paste the output here
```


### Additional Information

_No response_

--- 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/lib/_shape_base.py`
Content:
```
1 from numpy.lib import index_tricks
2 
3 import cupy
4 from cupy._core import internal
5 
6 
7 def apply_along_axis(func1d, axis, arr, *args, **kwargs):
8     """Apply a function to 1-D slices along the given axis.
9 
10     Args:
11         func1d (function (M,) -> (Nj...)): This function should accept 1-D
12             arrays. It is applied to 1-D slices of ``arr`` along the specified
13             axis. It must return a 1-D ``cupy.ndarray``.
14         axis (integer): Axis along which ``arr`` is sliced.
15         arr (cupy.ndarray (Ni..., M, Nk...)): Input array.
16         args: Additional arguments for ``func1d``.
17         kwargs: Additional keyword arguments for ``func1d``.
18 
19     Returns:
20         cupy.ndarray: The output array. The shape of ``out`` is identical to
21         the shape of ``arr``, except along the ``axis`` dimension. This
22         axis is removed, and replaced with new dimensions equal to the
23         shape of the return value of ``func1d``. So if ``func1d`` returns a
24         scalar ``out`` will have one fewer dimensions than ``arr``.
25 
26     .. seealso:: :func:`numpy.apply_along_axis`
27     """
28     ndim = arr.ndim
29     axis = internal._normalize_axis_index(axis, ndim)
30     inarr_view = cupy.moveaxis(arr, axis, -1)
31 
32     # compute indices for the iteration axes, and append a trailing ellipsis to
33     # prevent 0d arrays decaying to scalars
34     inds = index_tricks.ndindex(inarr_view.shape[:-1])
35     inds = (ind + (Ellipsis,) for ind in inds)
36 
37     # invoke the function on the first item
38     try:
39         ind0 = next(inds)
40     except StopIteration:
41         raise ValueError(
42             'Cannot apply_along_axis when any iteration dimensions are 0'
43         )
44     res = func1d(inarr_view[ind0], *args, **kwargs)
45     if cupy.isscalar(res):
46         # scalar outputs need to be transfered to a device ndarray
47         res = cupy.asarray(res)
48 
49     # build a buffer for storing evaluations of func1d.
50     # remove the requested axis, and add the new ones on the end.
51     # laid out so that each write is contiguous.
52     # for a tuple index inds, buff[inds] = func1d(inarr_view[inds])
53     buff = cupy.empty(inarr_view.shape[:-1] + res.shape, res.dtype)
54 
55     # save the first result, then compute and save all remaining results
56     buff[ind0] = res
57     for ind in inds:
58         buff[ind] = func1d(inarr_view[ind], *args, **kwargs)
59 
60     # restore the inserted axes back to where they belong
61     for i in range(res.ndim):
62         buff = cupy.moveaxis(buff, -1, axis)
63 
64     return buff
65
```
--- 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/lib/_shape_base.py b/cupy/lib/_shape_base.py
--- a/cupy/lib/_shape_base.py
+++ b/cupy/lib/_shape_base.py
@@ -42,9 +42,7 @@
             'Cannot apply_along_axis when any iteration dimensions are 0'
         )
     res = func1d(inarr_view[ind0], *args, **kwargs)
-    if cupy.isscalar(res):
-        # scalar outputs need to be transfered to a device ndarray
-        res = cupy.asarray(res)
+    res = cupy.asarray(res)
 
     # build a buffer for storing evaluations of func1d.
     # remove the requested axis, and add the new ones on the end.
@@ -55,7 +53,8 @@
     # save the first result, then compute and save all remaining results
     buff[ind0] = res
     for ind in inds:
-        buff[ind] = func1d(inarr_view[ind], *args, **kwargs)
+        out = func1d(inarr_view[ind], *args, **kwargs)
+        buff[ind] = cupy.asarray(out)
 
     # restore the inserted axes back to where they belong
     for i in range(res.ndim):

{"golden_diff": "diff --git a/cupy/lib/_shape_base.py b/cupy/lib/_shape_base.py\n--- a/cupy/lib/_shape_base.py\n+++ b/cupy/lib/_shape_base.py\n@@ -42,9 +42,7 @@\n             'Cannot apply_along_axis when any iteration dimensions are 0'\n         )\n     res = func1d(inarr_view[ind0], *args, **kwargs)\n-    if cupy.isscalar(res):\n-        # scalar outputs need to be transfered to a device ndarray\n-        res = cupy.asarray(res)\n+    res = cupy.asarray(res)\n \n     # build a buffer for storing evaluations of func1d.\n     # remove the requested axis, and add the new ones on the end.\n@@ -55,7 +53,8 @@\n     # save the first result, then compute and save all remaining results\n     buff[ind0] = res\n     for ind in inds:\n-        buff[ind] = func1d(inarr_view[ind], *args, **kwargs)\n+        out = func1d(inarr_view[ind], *args, **kwargs)\n+        buff[ind] = cupy.asarray(out)\n \n     # restore the inserted axes back to where they belong\n     for i in range(res.ndim):\n", "issue": "cupy.apply_along_axis failed with cupy.nonzero\n### Description\r\n\r\ncp.apply_along_axis(cp.nonzero, 1, cp.array([[1,2],[2,3]])) - failed with error\r\n\r\n*** AttributeError: 'tuple' object has no attribute 'shape'\r\n\r\nnp.apply_along_axis(np.nonzero, 1, np.array([[1,2],[2,3]])) - is OK\r\n\r\nUPDATE. Problem in _shape_base.py. \r\nline 53:\r\nbuff = cupy.empty(inarr_view.shape[:-1] + res.shape, res.dtype)\r\n\r\nres - is tuple (as a result of cp.nonzero(1d array) of single cupy-array, so line 44 ( if cupy.isscalar(res):) doesnt convert it from tuple to cupy-array\r\n\r\nas a temporal solution is possible to use \"buffer-like\" function\r\ndef cupy_nonzero (a):\r\n    return cp.nonzero(a)[0]\r\n\r\n### To Reproduce\r\n\r\n```py\r\ncp.apply_along_axis(cp.nonzero, 1, cp.array([[1,2],[2,3]]))\r\n```\r\n\r\n\r\n### Installation\r\n\r\n_No response_\r\n\r\n### Environment\r\n\r\n```\r\n# Paste the output here\r\n```\r\n\r\n\r\n### Additional Information\r\n\r\n_No response_\n", "before_files": [{"content": "from numpy.lib import index_tricks\n\nimport cupy\nfrom cupy._core import internal\n\n\ndef apply_along_axis(func1d, axis, arr, *args, **kwargs):\n    \"\"\"Apply a function to 1-D slices along the given axis.\n\n    Args:\n        func1d (function (M,) -> (Nj...)): This function should accept 1-D\n            arrays. It is applied to 1-D slices of ``arr`` along the specified\n            axis. It must return a 1-D ``cupy.ndarray``.\n        axis (integer): Axis along which ``arr`` is sliced.\n        arr (cupy.ndarray (Ni..., M, Nk...)): Input array.\n        args: Additional arguments for ``func1d``.\n        kwargs: Additional keyword arguments for ``func1d``.\n\n    Returns:\n        cupy.ndarray: The output array. The shape of ``out`` is identical to\n        the shape of ``arr``, except along the ``axis`` dimension. This\n        axis is removed, and replaced with new dimensions equal to the\n        shape of the return value of ``func1d``. So if ``func1d`` returns a\n        scalar ``out`` will have one fewer dimensions than ``arr``.\n\n    .. seealso:: :func:`numpy.apply_along_axis`\n    \"\"\"\n    ndim = arr.ndim\n    axis = internal._normalize_axis_index(axis, ndim)\n    inarr_view = cupy.moveaxis(arr, axis, -1)\n\n    # compute indices for the iteration axes, and append a trailing ellipsis to\n    # prevent 0d arrays decaying to scalars\n    inds = index_tricks.ndindex(inarr_view.shape[:-1])\n    inds = (ind + (Ellipsis,) for ind in inds)\n\n    # invoke the function on the first item\n    try:\n        ind0 = next(inds)\n    except StopIteration:\n        raise ValueError(\n            'Cannot apply_along_axis when any iteration dimensions are 0'\n        )\n    res = func1d(inarr_view[ind0], *args, **kwargs)\n    if cupy.isscalar(res):\n        # scalar outputs need to be transfered to a device ndarray\n        res = cupy.asarray(res)\n\n    # build a buffer for storing evaluations of func1d.\n    # remove the requested axis, and add the new ones on the end.\n    # laid out so that each write is contiguous.\n    # for a tuple index inds, buff[inds] = func1d(inarr_view[inds])\n    buff = cupy.empty(inarr_view.shape[:-1] + res.shape, res.dtype)\n\n    # save the first result, then compute and save all remaining results\n    buff[ind0] = res\n    for ind in inds:\n        buff[ind] = func1d(inarr_view[ind], *args, **kwargs)\n\n    # restore the inserted axes back to where they belong\n    for i in range(res.ndim):\n        buff = cupy.moveaxis(buff, -1, axis)\n\n    return buff\n", "path": "cupy/lib/_shape_base.py"}], "after_files": [{"content": "from numpy.lib import index_tricks\n\nimport cupy\nfrom cupy._core import internal\n\n\ndef apply_along_axis(func1d, axis, arr, *args, **kwargs):\n    \"\"\"Apply a function to 1-D slices along the given axis.\n\n    Args:\n        func1d (function (M,) -> (Nj...)): This function should accept 1-D\n            arrays. It is applied to 1-D slices of ``arr`` along the specified\n            axis. It must return a 1-D ``cupy.ndarray``.\n        axis (integer): Axis along which ``arr`` is sliced.\n        arr (cupy.ndarray (Ni..., M, Nk...)): Input array.\n        args: Additional arguments for ``func1d``.\n        kwargs: Additional keyword arguments for ``func1d``.\n\n    Returns:\n        cupy.ndarray: The output array. The shape of ``out`` is identical to\n        the shape of ``arr``, except along the ``axis`` dimension. This\n        axis is removed, and replaced with new dimensions equal to the\n        shape of the return value of ``func1d``. So if ``func1d`` returns a\n        scalar ``out`` will have one fewer dimensions than ``arr``.\n\n    .. seealso:: :func:`numpy.apply_along_axis`\n    \"\"\"\n    ndim = arr.ndim\n    axis = internal._normalize_axis_index(axis, ndim)\n    inarr_view = cupy.moveaxis(arr, axis, -1)\n\n    # compute indices for the iteration axes, and append a trailing ellipsis to\n    # prevent 0d arrays decaying to scalars\n    inds = index_tricks.ndindex(inarr_view.shape[:-1])\n    inds = (ind + (Ellipsis,) for ind in inds)\n\n    # invoke the function on the first item\n    try:\n        ind0 = next(inds)\n    except StopIteration:\n        raise ValueError(\n            'Cannot apply_along_axis when any iteration dimensions are 0'\n        )\n    res = func1d(inarr_view[ind0], *args, **kwargs)\n    res = cupy.asarray(res)\n\n    # build a buffer for storing evaluations of func1d.\n    # remove the requested axis, and add the new ones on the end.\n    # laid out so that each write is contiguous.\n    # for a tuple index inds, buff[inds] = func1d(inarr_view[inds])\n    buff = cupy.empty(inarr_view.shape[:-1] + res.shape, res.dtype)\n\n    # save the first result, then compute and save all remaining results\n    buff[ind0] = res\n    for ind in inds:\n        out = func1d(inarr_view[ind], *args, **kwargs)\n        buff[ind] = cupy.asarray(out)\n\n    # restore the inserted axes back to where they belong\n    for i in range(res.ndim):\n        buff = cupy.moveaxis(buff, -1, axis)\n\n    return buff\n", "path": "cupy/lib/_shape_base.py"}]}