prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  inconsistency between cupy.vstack and numpy.vstack
  ```
  In [1]: import cupy, numpy
  In [2]: a = cupy.arange(12).reshape(3, 4)
  In [3]: cupy.vstack([a])
  Out[3]: array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])
  In [4]: numpy.vstack([a.get()])
  Out[4]: 
  array([[ 0,  1,  2,  3],
         [ 4,  5,  6,  7],
         [ 8,  9, 10, 11]])
  ```
  
  
  --- 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/manipulation/join.py`
  Content:
  ```
  1 import numpy
  2 import six
  3 
  4 import cupy
  5 
  6 
  7 def column_stack(tup):
  8     """Stacks 1-D and 2-D arrays as columns into a 2-D array.
  9 
  10     A 1-D array is first converted to a 2-D column array. Then, the 2-D arrays
  11     are concatenated along the second axis.
  12 
  13     Args:
  14         tup (sequence of arrays): 1-D or 2-D arrays to be stacked.
  15 
  16     Returns:
  17         cupy.ndarray: A new 2-D array of stacked columns.
  18 
  19     .. seealso:: :func:`numpy.column_stack`
  20 
  21     """
  22     if any(not isinstance(a, cupy.ndarray) for a in tup):
  23         raise TypeError('Only cupy arrays can be column stacked')
  24 
  25     lst = list(tup)
  26     for i, a in enumerate(lst):
  27         if a.ndim == 1:
  28             a = a[:, cupy.newaxis]
  29             lst[i] = a
  30         elif a.ndim != 2:
  31             raise ValueError(
  32                 'Only 1 or 2 dimensional arrays can be column stacked')
  33 
  34     return concatenate(lst, axis=1)
  35 
  36 
  37 def concatenate(tup, axis=0):
  38     """Joins arrays along an axis.
  39 
  40     Args:
  41         tup (sequence of arrays): Arrays to be joined. All of these should have
  42             same dimensionalities except the specified axis.
  43         axis (int): The axis to join arrays along.
  44 
  45     Returns:
  46         cupy.ndarray: Joined array.
  47 
  48     .. seealso:: :func:`numpy.concatenate`
  49 
  50     """
  51     ndim = None
  52     shape = None
  53     for a in tup:
  54         if not isinstance(a, cupy.ndarray):
  55             raise TypeError('Only cupy arrays can be concatenated')
  56         if a.ndim == 0:
  57             raise TypeError('zero-dimensional arrays cannot be concatenated')
  58         if ndim is None:
  59             ndim = a.ndim
  60             shape = list(a.shape)
  61             axis = _get_positive_axis(a.ndim, axis)
  62             continue
  63 
  64         if a.ndim != ndim:
  65             raise ValueError(
  66                 'All arrays to concatenate must have the same ndim')
  67         if any(i != axis and shape[i] != a.shape[i]
  68                for i in six.moves.range(ndim)):
  69             raise ValueError(
  70                 'All arrays must have same shape except the axis to '
  71                 'concatenate')
  72         shape[axis] += a.shape[axis]
  73 
  74     if ndim is None:
  75         raise ValueError('Cannot concatenate from empty tuple')
  76 
  77     dtype = numpy.find_common_type([a.dtype for a in tup], [])
  78     ret = cupy.empty(shape, dtype=dtype)
  79 
  80     skip = (slice(None),) * axis
  81     i = 0
  82     for a in tup:
  83         aw = a.shape[axis]
  84         ret[skip + (slice(i, i + aw),)] = a
  85         i += aw
  86 
  87     return ret
  88 
  89 
  90 def dstack(tup):
  91     """Stacks arrays along the third axis.
  92 
  93     Args:
  94         tup (sequence of arrays): Arrays to be stacked. Each array is converted
  95             by :func:`cupy.atleast_3d` before stacking.
  96 
  97     Returns:
  98         cupy.ndarray: Stacked array.
  99 
  100     .. seealso:: :func:`numpy.dstack`
  101 
  102     """
  103     return concatenate(cupy.atleast_3d(*tup), 2)
  104 
  105 
  106 def hstack(tup):
  107     """Stacks arrays horizontally.
  108 
  109     If an input array has one dimension, then the array is treated as a
  110     horizontal vector and stacked along the first axis. Otherwise, the array is
  111     stacked along the second axis.
  112 
  113     Args:
  114         tup (sequence of arrays): Arrays to be stacked.
  115 
  116     Returns:
  117         cupy.ndarray: Stacked array.
  118 
  119     .. seealso:: :func:`numpy.hstack`
  120 
  121     """
  122     arrs = [cupy.atleast_1d(a) for a in tup]
  123     axis = 1
  124     if arrs[0].ndim == 1:
  125         axis = 0
  126     return concatenate(arrs, axis)
  127 
  128 
  129 def vstack(tup):
  130     """Stacks arrays vertically.
  131 
  132     If an input array has one dimension, then the array is treated as a
  133     horizontal vector and stacked along the additional axis at the head.
  134     Otherwise, the array is stacked along the first axis.
  135 
  136     Args:
  137         tup (sequence of arrays): Arrays to be stacked. Each array is converted
  138             by :func:`cupy.atleast_2d` before stacking.
  139 
  140     Returns:
  141         cupy.ndarray: Stacked array.
  142 
  143     .. seealso:: :func:`numpy.dstack`
  144 
  145     """
  146     return concatenate(cupy.atleast_2d(*tup), 0)
  147 
  148 
  149 def stack(tup, axis=0):
  150     """Stacks arrays along a new axis.
  151 
  152     Args:
  153         tup (sequence of arrays): Arrays to be stacked.
  154         axis (int): Axis along which the arrays are stacked.
  155 
  156     Returns:
  157         cupy.ndarray: Stacked array.
  158 
  159     .. seealso:: :func:`numpy.stack`
  160     """
  161     return concatenate([cupy.expand_dims(x, axis) for x in tup], axis)
  162 
  163 
  164 def _get_positive_axis(ndim, axis):
  165     a = axis
  166     if a < 0:
  167         a += ndim
  168     if a < 0 or a >= ndim:
  169         raise IndexError('axis {} out of bounds [0, {})'.format(axis, ndim))
  170     return a
  171
  ```
  --- 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/manipulation/join.py b/cupy/manipulation/join.py
  --- a/cupy/manipulation/join.py
  +++ b/cupy/manipulation/join.py
  @@ -143,7 +143,7 @@
       .. seealso:: :func:`numpy.dstack`
   
       """
  -    return concatenate(cupy.atleast_2d(*tup), 0)
  +    return concatenate([cupy.atleast_2d(m) for m in tup], 0)
   
   
   def stack(tup, axis=0):
  
verification_info: {"golden_diff": "diff --git a/cupy/manipulation/join.py b/cupy/manipulation/join.py\n--- a/cupy/manipulation/join.py\n+++ b/cupy/manipulation/join.py\n@@ -143,7 +143,7 @@\n     .. seealso:: :func:`numpy.dstack`\n \n     \"\"\"\n-    return concatenate(cupy.atleast_2d(*tup), 0)\n+    return concatenate([cupy.atleast_2d(m) for m in tup], 0)\n \n \n def stack(tup, axis=0):\n", "issue": "inconsistency between cupy.vstack and numpy.vstack\n```\nIn [1]: import cupy, numpy\nIn [2]: a = cupy.arange(12).reshape(3, 4)\nIn [3]: cupy.vstack([a])\nOut[3]: array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])\nIn [4]: numpy.vstack([a.get()])\nOut[4]: \narray([[ 0,  1,  2,  3],\n       [ 4,  5,  6,  7],\n       [ 8,  9, 10, 11]])\n```\n\n", "before_files": [{"content": "import numpy\nimport six\n\nimport cupy\n\n\ndef column_stack(tup):\n    \"\"\"Stacks 1-D and 2-D arrays as columns into a 2-D array.\n\n    A 1-D array is first converted to a 2-D column array. Then, the 2-D arrays\n    are concatenated along the second axis.\n\n    Args:\n        tup (sequence of arrays): 1-D or 2-D arrays to be stacked.\n\n    Returns:\n        cupy.ndarray: A new 2-D array of stacked columns.\n\n    .. seealso:: :func:`numpy.column_stack`\n\n    \"\"\"\n    if any(not isinstance(a, cupy.ndarray) for a in tup):\n        raise TypeError('Only cupy arrays can be column stacked')\n\n    lst = list(tup)\n    for i, a in enumerate(lst):\n        if a.ndim == 1:\n            a = a[:, cupy.newaxis]\n            lst[i] = a\n        elif a.ndim != 2:\n            raise ValueError(\n                'Only 1 or 2 dimensional arrays can be column stacked')\n\n    return concatenate(lst, axis=1)\n\n\ndef concatenate(tup, axis=0):\n    \"\"\"Joins arrays along an axis.\n\n    Args:\n        tup (sequence of arrays): Arrays to be joined. All of these should have\n            same dimensionalities except the specified axis.\n        axis (int): The axis to join arrays along.\n\n    Returns:\n        cupy.ndarray: Joined array.\n\n    .. seealso:: :func:`numpy.concatenate`\n\n    \"\"\"\n    ndim = None\n    shape = None\n    for a in tup:\n        if not isinstance(a, cupy.ndarray):\n            raise TypeError('Only cupy arrays can be concatenated')\n        if a.ndim == 0:\n            raise TypeError('zero-dimensional arrays cannot be concatenated')\n        if ndim is None:\n            ndim = a.ndim\n            shape = list(a.shape)\n            axis = _get_positive_axis(a.ndim, axis)\n            continue\n\n        if a.ndim != ndim:\n            raise ValueError(\n                'All arrays to concatenate must have the same ndim')\n        if any(i != axis and shape[i] != a.shape[i]\n               for i in six.moves.range(ndim)):\n            raise ValueError(\n                'All arrays must have same shape except the axis to '\n                'concatenate')\n        shape[axis] += a.shape[axis]\n\n    if ndim is None:\n        raise ValueError('Cannot concatenate from empty tuple')\n\n    dtype = numpy.find_common_type([a.dtype for a in tup], [])\n    ret = cupy.empty(shape, dtype=dtype)\n\n    skip = (slice(None),) * axis\n    i = 0\n    for a in tup:\n        aw = a.shape[axis]\n        ret[skip + (slice(i, i + aw),)] = a\n        i += aw\n\n    return ret\n\n\ndef dstack(tup):\n    \"\"\"Stacks arrays along the third axis.\n\n    Args:\n        tup (sequence of arrays): Arrays to be stacked. Each array is converted\n            by :func:`cupy.atleast_3d` before stacking.\n\n    Returns:\n        cupy.ndarray: Stacked array.\n\n    .. seealso:: :func:`numpy.dstack`\n\n    \"\"\"\n    return concatenate(cupy.atleast_3d(*tup), 2)\n\n\ndef hstack(tup):\n    \"\"\"Stacks arrays horizontally.\n\n    If an input array has one dimension, then the array is treated as a\n    horizontal vector and stacked along the first axis. Otherwise, the array is\n    stacked along the second axis.\n\n    Args:\n        tup (sequence of arrays): Arrays to be stacked.\n\n    Returns:\n        cupy.ndarray: Stacked array.\n\n    .. seealso:: :func:`numpy.hstack`\n\n    \"\"\"\n    arrs = [cupy.atleast_1d(a) for a in tup]\n    axis = 1\n    if arrs[0].ndim == 1:\n        axis = 0\n    return concatenate(arrs, axis)\n\n\ndef vstack(tup):\n    \"\"\"Stacks arrays vertically.\n\n    If an input array has one dimension, then the array is treated as a\n    horizontal vector and stacked along the additional axis at the head.\n    Otherwise, the array is stacked along the first axis.\n\n    Args:\n        tup (sequence of arrays): Arrays to be stacked. Each array is converted\n            by :func:`cupy.atleast_2d` before stacking.\n\n    Returns:\n        cupy.ndarray: Stacked array.\n\n    .. seealso:: :func:`numpy.dstack`\n\n    \"\"\"\n    return concatenate(cupy.atleast_2d(*tup), 0)\n\n\ndef stack(tup, axis=0):\n    \"\"\"Stacks arrays along a new axis.\n\n    Args:\n        tup (sequence of arrays): Arrays to be stacked.\n        axis (int): Axis along which the arrays are stacked.\n\n    Returns:\n        cupy.ndarray: Stacked array.\n\n    .. seealso:: :func:`numpy.stack`\n    \"\"\"\n    return concatenate([cupy.expand_dims(x, axis) for x in tup], axis)\n\n\ndef _get_positive_axis(ndim, axis):\n    a = axis\n    if a < 0:\n        a += ndim\n    if a < 0 or a >= ndim:\n        raise IndexError('axis {} out of bounds [0, {})'.format(axis, ndim))\n    return a\n", "path": "cupy/manipulation/join.py"}], "after_files": [{"content": "import numpy\nimport six\n\nimport cupy\n\n\ndef column_stack(tup):\n    \"\"\"Stacks 1-D and 2-D arrays as columns into a 2-D array.\n\n    A 1-D array is first converted to a 2-D column array. Then, the 2-D arrays\n    are concatenated along the second axis.\n\n    Args:\n        tup (sequence of arrays): 1-D or 2-D arrays to be stacked.\n\n    Returns:\n        cupy.ndarray: A new 2-D array of stacked columns.\n\n    .. seealso:: :func:`numpy.column_stack`\n\n    \"\"\"\n    if any(not isinstance(a, cupy.ndarray) for a in tup):\n        raise TypeError('Only cupy arrays can be column stacked')\n\n    lst = list(tup)\n    for i, a in enumerate(lst):\n        if a.ndim == 1:\n            a = a[:, cupy.newaxis]\n            lst[i] = a\n        elif a.ndim != 2:\n            raise ValueError(\n                'Only 1 or 2 dimensional arrays can be column stacked')\n\n    return concatenate(lst, axis=1)\n\n\ndef concatenate(tup, axis=0):\n    \"\"\"Joins arrays along an axis.\n\n    Args:\n        tup (sequence of arrays): Arrays to be joined. All of these should have\n            same dimensionalities except the specified axis.\n        axis (int): The axis to join arrays along.\n\n    Returns:\n        cupy.ndarray: Joined array.\n\n    .. seealso:: :func:`numpy.concatenate`\n\n    \"\"\"\n    ndim = None\n    shape = None\n    for a in tup:\n        if not isinstance(a, cupy.ndarray):\n            raise TypeError('Only cupy arrays can be concatenated')\n        if a.ndim == 0:\n            raise TypeError('zero-dimensional arrays cannot be concatenated')\n        if ndim is None:\n            ndim = a.ndim\n            shape = list(a.shape)\n            axis = _get_positive_axis(a.ndim, axis)\n            continue\n\n        if a.ndim != ndim:\n            raise ValueError(\n                'All arrays to concatenate must have the same ndim')\n        if any(i != axis and shape[i] != a.shape[i]\n               for i in six.moves.range(ndim)):\n            raise ValueError(\n                'All arrays must have same shape except the axis to '\n                'concatenate')\n        shape[axis] += a.shape[axis]\n\n    if ndim is None:\n        raise ValueError('Cannot concatenate from empty tuple')\n\n    dtype = numpy.find_common_type([a.dtype for a in tup], [])\n    ret = cupy.empty(shape, dtype=dtype)\n\n    skip = (slice(None),) * axis\n    i = 0\n    for a in tup:\n        aw = a.shape[axis]\n        ret[skip + (slice(i, i + aw),)] = a\n        i += aw\n\n    return ret\n\n\ndef dstack(tup):\n    \"\"\"Stacks arrays along the third axis.\n\n    Args:\n        tup (sequence of arrays): Arrays to be stacked. Each array is converted\n            by :func:`cupy.atleast_3d` before stacking.\n\n    Returns:\n        cupy.ndarray: Stacked array.\n\n    .. seealso:: :func:`numpy.dstack`\n\n    \"\"\"\n    return concatenate(cupy.atleast_3d(*tup), 2)\n\n\ndef hstack(tup):\n    \"\"\"Stacks arrays horizontally.\n\n    If an input array has one dimension, then the array is treated as a\n    horizontal vector and stacked along the first axis. Otherwise, the array is\n    stacked along the second axis.\n\n    Args:\n        tup (sequence of arrays): Arrays to be stacked.\n\n    Returns:\n        cupy.ndarray: Stacked array.\n\n    .. seealso:: :func:`numpy.hstack`\n\n    \"\"\"\n    arrs = [cupy.atleast_1d(a) for a in tup]\n    axis = 1\n    if arrs[0].ndim == 1:\n        axis = 0\n    return concatenate(arrs, axis)\n\n\ndef vstack(tup):\n    \"\"\"Stacks arrays vertically.\n\n    If an input array has one dimension, then the array is treated as a\n    horizontal vector and stacked along the additional axis at the head.\n    Otherwise, the array is stacked along the first axis.\n\n    Args:\n        tup (sequence of arrays): Arrays to be stacked. Each array is converted\n            by :func:`cupy.atleast_2d` before stacking.\n\n    Returns:\n        cupy.ndarray: Stacked array.\n\n    .. seealso:: :func:`numpy.dstack`\n\n    \"\"\"\n    return concatenate([cupy.atleast_2d(m) for m in tup], 0)\n\n\ndef stack(tup, axis=0):\n    \"\"\"Stacks arrays along a new axis.\n\n    Args:\n        tup (sequence of arrays): Arrays to be stacked.\n        axis (int): Axis along which the arrays are stacked.\n\n    Returns:\n        cupy.ndarray: Stacked array.\n\n    .. seealso:: :func:`numpy.stack`\n    \"\"\"\n    return concatenate([cupy.expand_dims(x, axis) for x in tup], axis)\n\n\ndef _get_positive_axis(ndim, axis):\n    a = axis\n    if a < 0:\n        a += ndim\n    if a < 0 or a >= ndim:\n        raise IndexError('axis {} out of bounds [0, {})'.format(axis, ndim))\n    return a\n", "path": "cupy/manipulation/join.py"}]}