We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
`cupy.concatenate()` misses arguments `dtype` and `casting`
Refs:
- NumPy: https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html
- CuPy: https://docs.cupy.dev/en/stable/reference/generated/cupy.concatenate.html

The `dtype` argument is needed by the Array API standard (#5698, #4789).

--- 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 cupy
2 from cupy import _core
3 
4 
5 def column_stack(tup):
6     """Stacks 1-D and 2-D arrays as columns into a 2-D array.
7 
8     A 1-D array is first converted to a 2-D column array. Then, the 2-D arrays
9     are concatenated along the second axis.
10 
11     Args:
12         tup (sequence of arrays): 1-D or 2-D arrays to be stacked.
13 
14     Returns:
15         cupy.ndarray: A new 2-D array of stacked columns.
16 
17     .. seealso:: :func:`numpy.column_stack`
18 
19     """
20     if any(not isinstance(a, cupy.ndarray) for a in tup):
21         raise TypeError('Only cupy arrays can be column stacked')
22 
23     lst = list(tup)
24     for i, a in enumerate(lst):
25         if a.ndim == 1:
26             a = a[:, cupy.newaxis]
27             lst[i] = a
28         elif a.ndim != 2:
29             raise ValueError(
30                 'Only 1 or 2 dimensional arrays can be column stacked')
31 
32     return concatenate(lst, axis=1)
33 
34 
35 def concatenate(tup, axis=0, out=None):
36     """Joins arrays along an axis.
37 
38     Args:
39         tup (sequence of arrays): Arrays to be joined. All of these should have
40             same dimensionalities except the specified axis.
41         axis (int or None): The axis to join arrays along.
42             If axis is None, arrays are flattened before use.
43             Default is 0.
44         out (cupy.ndarray): Output array.
45 
46     Returns:
47         cupy.ndarray: Joined array.
48 
49     .. seealso:: :func:`numpy.concatenate`
50 
51     """
52     if axis is None:
53         tup = [m.ravel() for m in tup]
54         axis = 0
55     return _core.concatenate_method(tup, axis, out)
56 
57 
58 def dstack(tup):
59     """Stacks arrays along the third axis.
60 
61     Args:
62         tup (sequence of arrays): Arrays to be stacked. Each array is converted
63             by :func:`cupy.atleast_3d` before stacking.
64 
65     Returns:
66         cupy.ndarray: Stacked array.
67 
68     .. seealso:: :func:`numpy.dstack`
69 
70     """
71     return concatenate([cupy.atleast_3d(m) for m in tup], 2)
72 
73 
74 def hstack(tup):
75     """Stacks arrays horizontally.
76 
77     If an input array has one dimension, then the array is treated as a
78     horizontal vector and stacked along the first axis. Otherwise, the array is
79     stacked along the second axis.
80 
81     Args:
82         tup (sequence of arrays): Arrays to be stacked.
83 
84     Returns:
85         cupy.ndarray: Stacked array.
86 
87     .. seealso:: :func:`numpy.hstack`
88 
89     """
90     arrs = [cupy.atleast_1d(a) for a in tup]
91     axis = 1
92     if arrs[0].ndim == 1:
93         axis = 0
94     return concatenate(arrs, axis)
95 
96 
97 def vstack(tup):
98     """Stacks arrays vertically.
99 
100     If an input array has one dimension, then the array is treated as a
101     horizontal vector and stacked along the additional axis at the head.
102     Otherwise, the array is stacked along the first axis.
103 
104     Args:
105         tup (sequence of arrays): Arrays to be stacked. Each array is converted
106             by :func:`cupy.atleast_2d` before stacking.
107 
108     Returns:
109         cupy.ndarray: Stacked array.
110 
111     .. seealso:: :func:`numpy.dstack`
112 
113     """
114     return concatenate([cupy.atleast_2d(m) for m in tup], 0)
115 
116 
117 def stack(tup, axis=0, out=None):
118     """Stacks arrays along a new axis.
119 
120     Args:
121         tup (sequence of arrays): Arrays to be stacked.
122         axis (int): Axis along which the arrays are stacked.
123         out (cupy.ndarray): Output array.
124 
125     Returns:
126         cupy.ndarray: Stacked array.
127 
128     .. seealso:: :func:`numpy.stack`
129     """
130     return concatenate([cupy.expand_dims(x, axis) for x in tup], axis, out)
131
```
--- 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/_manipulation/join.py b/cupy/_manipulation/join.py
--- a/cupy/_manipulation/join.py
+++ b/cupy/_manipulation/join.py
@@ -32,7 +32,7 @@
     return concatenate(lst, axis=1)
 
 
-def concatenate(tup, axis=0, out=None):
+def concatenate(tup, axis=0, out=None, *, dtype=None, casting='same_kind'):
     """Joins arrays along an axis.
 
     Args:
@@ -42,6 +42,11 @@
             If axis is None, arrays are flattened before use.
             Default is 0.
         out (cupy.ndarray): Output array.
+        dtype (str or dtype): If provided, the destination array will have this
+            dtype. Cannot be provided together with ``out``.
+        casting ({‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional):
+            Controls what kind of data casting may occur. Defaults to
+            ``'same_kind'``.
 
     Returns:
         cupy.ndarray: Joined array.
@@ -52,7 +57,7 @@
     if axis is None:
         tup = [m.ravel() for m in tup]
         axis = 0
-    return _core.concatenate_method(tup, axis, out)
+    return _core.concatenate_method(tup, axis, out, dtype, casting)
 
 
 def dstack(tup):

{"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@@ -32,7 +32,7 @@\n     return concatenate(lst, axis=1)\n \n \n-def concatenate(tup, axis=0, out=None):\n+def concatenate(tup, axis=0, out=None, *, dtype=None, casting='same_kind'):\n     \"\"\"Joins arrays along an axis.\n \n     Args:\n@@ -42,6 +42,11 @@\n             If axis is None, arrays are flattened before use.\n             Default is 0.\n         out (cupy.ndarray): Output array.\n+        dtype (str or dtype): If provided, the destination array will have this\n+            dtype. Cannot be provided together with ``out``.\n+        casting ({\u2018no\u2019, \u2018equiv\u2019, \u2018safe\u2019, \u2018same_kind\u2019, \u2018unsafe\u2019}, optional):\n+            Controls what kind of data casting may occur. Defaults to\n+            ``'same_kind'``.\n \n     Returns:\n         cupy.ndarray: Joined array.\n@@ -52,7 +57,7 @@\n     if axis is None:\n         tup = [m.ravel() for m in tup]\n         axis = 0\n-    return _core.concatenate_method(tup, axis, out)\n+    return _core.concatenate_method(tup, axis, out, dtype, casting)\n \n \n def dstack(tup):\n", "issue": "`cupy.concatenate()` misses arguments `dtype` and `casting`\nRefs:\r\n- NumPy: https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html\r\n- CuPy: https://docs.cupy.dev/en/stable/reference/generated/cupy.concatenate.html\r\n\r\nThe `dtype` argument is needed by the Array API standard (#5698, #4789).\n", "before_files": [{"content": "import cupy\nfrom cupy import _core\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, out=None):\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 or None): The axis to join arrays along.\n            If axis is None, arrays are flattened before use.\n            Default is 0.\n        out (cupy.ndarray): Output array.\n\n    Returns:\n        cupy.ndarray: Joined array.\n\n    .. seealso:: :func:`numpy.concatenate`\n\n    \"\"\"\n    if axis is None:\n        tup = [m.ravel() for m in tup]\n        axis = 0\n    return _core.concatenate_method(tup, axis, out)\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(m) for m in 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, out=None):\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        out (cupy.ndarray): Output array.\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, out)\n", "path": "cupy/_manipulation/join.py"}], "after_files": [{"content": "import cupy\nfrom cupy import _core\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, out=None, *, dtype=None, casting='same_kind'):\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 or None): The axis to join arrays along.\n            If axis is None, arrays are flattened before use.\n            Default is 0.\n        out (cupy.ndarray): Output array.\n        dtype (str or dtype): If provided, the destination array will have this\n            dtype. Cannot be provided together with ``out``.\n        casting ({\u2018no\u2019, \u2018equiv\u2019, \u2018safe\u2019, \u2018same_kind\u2019, \u2018unsafe\u2019}, optional):\n            Controls what kind of data casting may occur. Defaults to\n            ``'same_kind'``.\n\n    Returns:\n        cupy.ndarray: Joined array.\n\n    .. seealso:: :func:`numpy.concatenate`\n\n    \"\"\"\n    if axis is None:\n        tup = [m.ravel() for m in tup]\n        axis = 0\n    return _core.concatenate_method(tup, axis, out, dtype, casting)\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(m) for m in 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, out=None):\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        out (cupy.ndarray): Output array.\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, out)\n", "path": "cupy/_manipulation/join.py"}]}