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