We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
column_stack


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `ivy/functional/frontends/jax/numpy/manipulations.py`
Content:
```
1 # local
2 import ivy
3 from ivy.functional.frontends.jax.func_wrapper import (
4     to_ivy_arrays_and_back,
5     handle_jax_dtype,
6 )
7 from ivy.functional.frontends.jax.numpy import promote_types_of_jax_inputs
8 
9 
10 @to_ivy_arrays_and_back
11 def clip(a, a_min=None, a_max=None, out=None):
12     ivy.utils.assertions.check_all_or_any_fn(
13         a_min,
14         a_max,
15         fn=ivy.exists,
16         type="any",
17         limit=[1, 2],
18         message="at most one of a_min or a_max can be None",
19     )
20     a = ivy.array(a)
21     if a_min is None:
22         a, a_max = promote_types_of_jax_inputs(a, a_max)
23         return ivy.minimum(a, a_max, out=out)
24     if a_max is None:
25         a, a_min = promote_types_of_jax_inputs(a, a_min)
26         return ivy.maximum(a, a_min, out=out)
27     return ivy.clip(a, a_min, a_max, out=out)
28 
29 
30 @handle_jax_dtype
31 @to_ivy_arrays_and_back
32 def concatenate(arrays, axis=0, dtype=None):
33     ret = ivy.concat(arrays, axis=axis)
34     if dtype:
35         ret = ivy.array(ret, dtype=dtype)
36     return ret
37 
38 
39 @to_ivy_arrays_and_back
40 def repeat(a, repeats, axis=None, *, total_repeat_length=None):
41     return ivy.repeat(a, repeats, axis=axis)
42 
43 
44 @to_ivy_arrays_and_back
45 def reshape(a, newshape, order="C"):
46     return ivy.reshape(a, shape=newshape, order=order)
47 
48 
49 @to_ivy_arrays_and_back
50 def ravel(a, order="C"):
51     return ivy.reshape(a, shape=(-1,), order=order)
52 
53 
54 @to_ivy_arrays_and_back
55 def resize(a, new_shape):
56     a = ivy.array(a)
57     resized_a = ivy.reshape(a, new_shape)
58     return resized_a
59 
60 
61 @to_ivy_arrays_and_back
62 def moveaxis(a, source, destination):
63     return ivy.moveaxis(a, source, destination)
64 
65 
66 @to_ivy_arrays_and_back
67 def flipud(m):
68     return ivy.flipud(m, out=None)
69 
70 
71 @to_ivy_arrays_and_back
72 def transpose(a, axes=None):
73     if ivy.isscalar(a):
74         return ivy.array(a)
75     elif a.ndim == 1:
76         return a
77     if not axes:
78         axes = list(range(len(a.shape)))[::-1]
79     if type(axes) is int:
80         axes = [axes]
81     if (len(a.shape) == 0 and not axes) or (len(a.shape) == 1 and axes[0] == 0):
82         return a
83     return ivy.permute_dims(a, axes, out=None)
84 
85 
86 @to_ivy_arrays_and_back
87 def flip(m, axis=None):
88     return ivy.flip(m, axis=axis)
89 
90 
91 @to_ivy_arrays_and_back
92 def fliplr(m):
93     return ivy.fliplr(m)
94 
95 
96 @to_ivy_arrays_and_back
97 def expand_dims(a, axis):
98     return ivy.expand_dims(a, axis=axis)
99 
100 
101 @to_ivy_arrays_and_back
102 def stack(arrays, axis=0, out=None, dtype=None):
103     if dtype:
104         return ivy.astype(
105             ivy.stack(arrays, axis=axis, out=out), ivy.as_ivy_dtype(dtype)
106         )
107     return ivy.stack(arrays, axis=axis, out=out)
108 
109 
110 @to_ivy_arrays_and_back
111 def take(
112     a,
113     indices,
114     axis=None,
115     out=None,
116     mode=None,
117     unique_indices=False,
118     indices_are_sorted=False,
119     fill_value=None,
120 ):
121     return ivy.gather(a, indices, axis=axis, out=out)
122 
123 
124 @to_ivy_arrays_and_back
125 def broadcast_arrays(*args):
126     return ivy.broadcast_arrays(*args)
127 
128 
129 @to_ivy_arrays_and_back
130 def broadcast_shapes(*shapes):
131     return ivy.broadcast_shapes(*shapes)
132 
133 
134 @to_ivy_arrays_and_back
135 def broadcast_to(array, shape):
136     return ivy.broadcast_to(array, shape)
137 
138 
139 @to_ivy_arrays_and_back
140 def append(arr, values, axis=None):
141     if axis is None:
142         return ivy.concat((ivy.flatten(arr), ivy.flatten(values)), axis=0)
143     else:
144         return ivy.concat((arr, values), axis=axis)
145 
146 
147 @to_ivy_arrays_and_back
148 def swapaxes(a, axis1, axis2):
149     return ivy.swapaxes(a, axis1, axis2)
150 
151 
152 @to_ivy_arrays_and_back
153 def atleast_3d(*arys):
154     return ivy.atleast_3d(*arys)
155 
156 
157 @to_ivy_arrays_and_back
158 def atleast_1d(*arys):
159     return ivy.atleast_1d(*arys)
160 
161 
162 @to_ivy_arrays_and_back
163 def atleast_2d(*arys):
164     return ivy.atleast_2d(*arys)
165 
166 
167 @to_ivy_arrays_and_back
168 def tril(m, k=0):
169     return ivy.tril(m, k=k)
170 
171 
172 @to_ivy_arrays_and_back
173 def block(arr):
174     # TODO: reimplement block
175     raise ivy.utils.exceptions.IvyNotImplementedError()
176 
177 
178 @to_ivy_arrays_and_back
179 def squeeze(a, axis=None):
180     return ivy.squeeze(a, axis=axis)
181 
182 
183 @to_ivy_arrays_and_back
184 def rot90(m, k=1, axes=(0, 1)):
185     return ivy.rot90(m, k=k, axes=axes)
186 
187 
188 @to_ivy_arrays_and_back
189 def split(ary, indices_or_sections, axis=0):
190     if isinstance(indices_or_sections, (list, tuple, ivy.Array)):
191         indices_or_sections = (
192             ivy.diff(indices_or_sections, prepend=[0], append=[ary.shape[axis]])
193             .astype(ivy.int8)
194             .to_list()
195         )
196     return ivy.split(
197         ary, num_or_size_splits=indices_or_sections, axis=axis, with_remainder=False
198     )
199 
200 
201 @to_ivy_arrays_and_back
202 def array_split(ary, indices_or_sections, axis=0):
203     return ivy.split(
204         ary, num_or_size_splits=indices_or_sections, axis=axis, with_remainder=True
205     )
206 
207 
208 @to_ivy_arrays_and_back
209 def tile(A, reps):
210     return ivy.tile(A, reps)
211 
212 
213 @to_ivy_arrays_and_back
214 def dsplit(ary, indices_or_sections):
215     if isinstance(indices_or_sections, (list, tuple, ivy.Array)):
216         indices_or_sections = (
217             ivy.diff(indices_or_sections, prepend=[0], append=[ary.shape[2]])
218             .astype(ivy.int8)
219             .to_list()
220         )
221     return ivy.dsplit(ary, indices_or_sections)
222 
223 
224 @to_ivy_arrays_and_back
225 def dstack(tup, dtype=None):
226     return ivy.dstack(tup)
227 
228 
229 @to_ivy_arrays_and_back
230 def vsplit(ary, indices_or_sections):
231     if isinstance(indices_or_sections, (list, tuple, ivy.Array)):
232         indices_or_sections = (
233             ivy.diff(indices_or_sections, prepend=[0], append=[ary.shape[0]])
234             .astype(ivy.int8)
235             .to_list()
236         )
237     return ivy.vsplit(ary, indices_or_sections)
238 
239 
240 @to_ivy_arrays_and_back
241 def hsplit(ary, indices_or_sections):
242     if isinstance(indices_or_sections, (list, tuple, ivy.Array)):
243         if ary.ndim == 1:
244             indices_or_sections = (
245                 ivy.diff(indices_or_sections, prepend=[0], append=[ary.shape[0]])
246                 .astype(ivy.int8)
247                 .to_list()
248             )
249         else:
250             indices_or_sections = (
251                 ivy.diff(indices_or_sections, prepend=[0], append=[ary.shape[1]])
252                 .astype(ivy.int8)
253                 .to_list()
254             )
255     return ivy.hsplit(ary, indices_or_sections)
256 
257 
258 @to_ivy_arrays_and_back
259 def roll(a, shift, axis=None):
260     return ivy.roll(a, shift, axis=axis)
261 
262 
263 @to_ivy_arrays_and_back
264 def row_stack(tup):
265     if len(ivy.shape(tup[0])) == 1:
266         xs = []
267         for t in tup:
268             xs += [ivy.reshape(t, (1, ivy.shape(t)[0]))]
269         return ivy.concat(xs, axis=0)
270     return ivy.concat(tup, axis=0)
271 
272 
273 @to_ivy_arrays_and_back
274 def pad(array, pad_width, mode="constant", **kwargs):
275     return ivy.pad(array, pad_width, mode=mode, **kwargs)
276 
277 
278 def hamming(M):
279     if M <= 1:
280         return ivy.ones([M], dtype=ivy.float64)
281     n = ivy.arange(M)
282     ret = 0.54 - 0.46 * ivy.cos(2.0 * ivy.pi * n / (M - 1))
283     return ret
284 
285 
286 @to_ivy_arrays_and_back
287 def hanning(M):
288     if M <= 1:
289         return ivy.ones([M], dtype=ivy.float64)
290     n = ivy.arange(M)
291     ret = 0.5 * (1 - ivy.cos(2.0 * ivy.pi * n / (M - 1)))
292     return ret
293 
294 
295 @to_ivy_arrays_and_back
296 def kaiser(M, beta):
297     if M <= 1:
298         return ivy.ones([M], dtype=ivy.float64)
299     n = ivy.arange(M)
300     alpha = 0.5 * (M - 1)
301     ret = ivy.i0(beta * ivy.sqrt(1 - ((n - alpha) / alpha) ** 2)) / ivy.i0(beta)
302     return ret
303 
304 
305 @handle_jax_dtype
306 @to_ivy_arrays_and_back
307 def tri(N, M=None, k=0, dtype="float64"):
308     if M is None:
309         M = N
310     ones = ivy.ones((N, M), dtype=dtype)
311     return ivy.tril(ones, k=k)
312 
313 
314 @to_ivy_arrays_and_back
315 def blackman(M):
316     if M < 1:
317         return ivy.array([])
318     if M == 1:
319         return ivy.ones((1,))
320     n = ivy.arange(0, M)
321     alpha = 0.16
322     a0 = (1 - alpha) / 2
323     a1 = 1 / 2
324     a2 = alpha / 2
325     ret = (
326         a0
327         - a1 * ivy.cos(2 * ivy.pi * n / (M - 1))
328         + a2 * ivy.cos(4 * ivy.pi * n / (M - 1))
329     )
330     return ret
331
```
--- 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/ivy/functional/frontends/jax/numpy/manipulations.py b/ivy/functional/frontends/jax/numpy/manipulations.py
--- a/ivy/functional/frontends/jax/numpy/manipulations.py
+++ b/ivy/functional/frontends/jax/numpy/manipulations.py
@@ -270,6 +270,16 @@
     return ivy.concat(tup, axis=0)
 
 
+@to_ivy_arrays_and_back
+def column_stack(tup):
+    if len(ivy.shape(tup[0])) == 1:
+        ys = []
+        for t in tup:
+            ys += [ivy.reshape(t, (ivy.shape(t)[0], 1))]
+        return ivy.concat(ys, axis=1)
+    return ivy.concat(tup, axis=1)
+
+
 @to_ivy_arrays_and_back
 def pad(array, pad_width, mode="constant", **kwargs):
     return ivy.pad(array, pad_width, mode=mode, **kwargs)

{"golden_diff": "diff --git a/ivy/functional/frontends/jax/numpy/manipulations.py b/ivy/functional/frontends/jax/numpy/manipulations.py\n--- a/ivy/functional/frontends/jax/numpy/manipulations.py\n+++ b/ivy/functional/frontends/jax/numpy/manipulations.py\n@@ -270,6 +270,16 @@\n     return ivy.concat(tup, axis=0)\n \n \n+@to_ivy_arrays_and_back\n+def column_stack(tup):\n+    if len(ivy.shape(tup[0])) == 1:\n+        ys = []\n+        for t in tup:\n+            ys += [ivy.reshape(t, (ivy.shape(t)[0], 1))]\n+        return ivy.concat(ys, axis=1)\n+    return ivy.concat(tup, axis=1)\n+\n+\n @to_ivy_arrays_and_back\n def pad(array, pad_width, mode=\"constant\", **kwargs):\n     return ivy.pad(array, pad_width, mode=mode, **kwargs)\n", "issue": "column_stack\n\n", "before_files": [{"content": "# local\nimport ivy\nfrom ivy.functional.frontends.jax.func_wrapper import (\n    to_ivy_arrays_and_back,\n    handle_jax_dtype,\n)\nfrom ivy.functional.frontends.jax.numpy import promote_types_of_jax_inputs\n\n\n@to_ivy_arrays_and_back\ndef clip(a, a_min=None, a_max=None, out=None):\n    ivy.utils.assertions.check_all_or_any_fn(\n        a_min,\n        a_max,\n        fn=ivy.exists,\n        type=\"any\",\n        limit=[1, 2],\n        message=\"at most one of a_min or a_max can be None\",\n    )\n    a = ivy.array(a)\n    if a_min is None:\n        a, a_max = promote_types_of_jax_inputs(a, a_max)\n        return ivy.minimum(a, a_max, out=out)\n    if a_max is None:\n        a, a_min = promote_types_of_jax_inputs(a, a_min)\n        return ivy.maximum(a, a_min, out=out)\n    return ivy.clip(a, a_min, a_max, out=out)\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\ndef concatenate(arrays, axis=0, dtype=None):\n    ret = ivy.concat(arrays, axis=axis)\n    if dtype:\n        ret = ivy.array(ret, dtype=dtype)\n    return ret\n\n\n@to_ivy_arrays_and_back\ndef repeat(a, repeats, axis=None, *, total_repeat_length=None):\n    return ivy.repeat(a, repeats, axis=axis)\n\n\n@to_ivy_arrays_and_back\ndef reshape(a, newshape, order=\"C\"):\n    return ivy.reshape(a, shape=newshape, order=order)\n\n\n@to_ivy_arrays_and_back\ndef ravel(a, order=\"C\"):\n    return ivy.reshape(a, shape=(-1,), order=order)\n\n\n@to_ivy_arrays_and_back\ndef resize(a, new_shape):\n    a = ivy.array(a)\n    resized_a = ivy.reshape(a, new_shape)\n    return resized_a\n\n\n@to_ivy_arrays_and_back\ndef moveaxis(a, source, destination):\n    return ivy.moveaxis(a, source, destination)\n\n\n@to_ivy_arrays_and_back\ndef flipud(m):\n    return ivy.flipud(m, out=None)\n\n\n@to_ivy_arrays_and_back\ndef transpose(a, axes=None):\n    if ivy.isscalar(a):\n        return ivy.array(a)\n    elif a.ndim == 1:\n        return a\n    if not axes:\n        axes = list(range(len(a.shape)))[::-1]\n    if type(axes) is int:\n        axes = [axes]\n    if (len(a.shape) == 0 and not axes) or (len(a.shape) == 1 and axes[0] == 0):\n        return a\n    return ivy.permute_dims(a, axes, out=None)\n\n\n@to_ivy_arrays_and_back\ndef flip(m, axis=None):\n    return ivy.flip(m, axis=axis)\n\n\n@to_ivy_arrays_and_back\ndef fliplr(m):\n    return ivy.fliplr(m)\n\n\n@to_ivy_arrays_and_back\ndef expand_dims(a, axis):\n    return ivy.expand_dims(a, axis=axis)\n\n\n@to_ivy_arrays_and_back\ndef stack(arrays, axis=0, out=None, dtype=None):\n    if dtype:\n        return ivy.astype(\n            ivy.stack(arrays, axis=axis, out=out), ivy.as_ivy_dtype(dtype)\n        )\n    return ivy.stack(arrays, axis=axis, out=out)\n\n\n@to_ivy_arrays_and_back\ndef take(\n    a,\n    indices,\n    axis=None,\n    out=None,\n    mode=None,\n    unique_indices=False,\n    indices_are_sorted=False,\n    fill_value=None,\n):\n    return ivy.gather(a, indices, axis=axis, out=out)\n\n\n@to_ivy_arrays_and_back\ndef broadcast_arrays(*args):\n    return ivy.broadcast_arrays(*args)\n\n\n@to_ivy_arrays_and_back\ndef broadcast_shapes(*shapes):\n    return ivy.broadcast_shapes(*shapes)\n\n\n@to_ivy_arrays_and_back\ndef broadcast_to(array, shape):\n    return ivy.broadcast_to(array, shape)\n\n\n@to_ivy_arrays_and_back\ndef append(arr, values, axis=None):\n    if axis is None:\n        return ivy.concat((ivy.flatten(arr), ivy.flatten(values)), axis=0)\n    else:\n        return ivy.concat((arr, values), axis=axis)\n\n\n@to_ivy_arrays_and_back\ndef swapaxes(a, axis1, axis2):\n    return ivy.swapaxes(a, axis1, axis2)\n\n\n@to_ivy_arrays_and_back\ndef atleast_3d(*arys):\n    return ivy.atleast_3d(*arys)\n\n\n@to_ivy_arrays_and_back\ndef atleast_1d(*arys):\n    return ivy.atleast_1d(*arys)\n\n\n@to_ivy_arrays_and_back\ndef atleast_2d(*arys):\n    return ivy.atleast_2d(*arys)\n\n\n@to_ivy_arrays_and_back\ndef tril(m, k=0):\n    return ivy.tril(m, k=k)\n\n\n@to_ivy_arrays_and_back\ndef block(arr):\n    # TODO: reimplement block\n    raise ivy.utils.exceptions.IvyNotImplementedError()\n\n\n@to_ivy_arrays_and_back\ndef squeeze(a, axis=None):\n    return ivy.squeeze(a, axis=axis)\n\n\n@to_ivy_arrays_and_back\ndef rot90(m, k=1, axes=(0, 1)):\n    return ivy.rot90(m, k=k, axes=axes)\n\n\n@to_ivy_arrays_and_back\ndef split(ary, indices_or_sections, axis=0):\n    if isinstance(indices_or_sections, (list, tuple, ivy.Array)):\n        indices_or_sections = (\n            ivy.diff(indices_or_sections, prepend=[0], append=[ary.shape[axis]])\n            .astype(ivy.int8)\n            .to_list()\n        )\n    return ivy.split(\n        ary, num_or_size_splits=indices_or_sections, axis=axis, with_remainder=False\n    )\n\n\n@to_ivy_arrays_and_back\ndef array_split(ary, indices_or_sections, axis=0):\n    return ivy.split(\n        ary, num_or_size_splits=indices_or_sections, axis=axis, with_remainder=True\n    )\n\n\n@to_ivy_arrays_and_back\ndef tile(A, reps):\n    return ivy.tile(A, reps)\n\n\n@to_ivy_arrays_and_back\ndef dsplit(ary, indices_or_sections):\n    if isinstance(indices_or_sections, (list, tuple, ivy.Array)):\n        indices_or_sections = (\n            ivy.diff(indices_or_sections, prepend=[0], append=[ary.shape[2]])\n            .astype(ivy.int8)\n            .to_list()\n        )\n    return ivy.dsplit(ary, indices_or_sections)\n\n\n@to_ivy_arrays_and_back\ndef dstack(tup, dtype=None):\n    return ivy.dstack(tup)\n\n\n@to_ivy_arrays_and_back\ndef vsplit(ary, indices_or_sections):\n    if isinstance(indices_or_sections, (list, tuple, ivy.Array)):\n        indices_or_sections = (\n            ivy.diff(indices_or_sections, prepend=[0], append=[ary.shape[0]])\n            .astype(ivy.int8)\n            .to_list()\n        )\n    return ivy.vsplit(ary, indices_or_sections)\n\n\n@to_ivy_arrays_and_back\ndef hsplit(ary, indices_or_sections):\n    if isinstance(indices_or_sections, (list, tuple, ivy.Array)):\n        if ary.ndim == 1:\n            indices_or_sections = (\n                ivy.diff(indices_or_sections, prepend=[0], append=[ary.shape[0]])\n                .astype(ivy.int8)\n                .to_list()\n            )\n        else:\n            indices_or_sections = (\n                ivy.diff(indices_or_sections, prepend=[0], append=[ary.shape[1]])\n                .astype(ivy.int8)\n                .to_list()\n            )\n    return ivy.hsplit(ary, indices_or_sections)\n\n\n@to_ivy_arrays_and_back\ndef roll(a, shift, axis=None):\n    return ivy.roll(a, shift, axis=axis)\n\n\n@to_ivy_arrays_and_back\ndef row_stack(tup):\n    if len(ivy.shape(tup[0])) == 1:\n        xs = []\n        for t in tup:\n            xs += [ivy.reshape(t, (1, ivy.shape(t)[0]))]\n        return ivy.concat(xs, axis=0)\n    return ivy.concat(tup, axis=0)\n\n\n@to_ivy_arrays_and_back\ndef pad(array, pad_width, mode=\"constant\", **kwargs):\n    return ivy.pad(array, pad_width, mode=mode, **kwargs)\n\n\ndef hamming(M):\n    if M <= 1:\n        return ivy.ones([M], dtype=ivy.float64)\n    n = ivy.arange(M)\n    ret = 0.54 - 0.46 * ivy.cos(2.0 * ivy.pi * n / (M - 1))\n    return ret\n\n\n@to_ivy_arrays_and_back\ndef hanning(M):\n    if M <= 1:\n        return ivy.ones([M], dtype=ivy.float64)\n    n = ivy.arange(M)\n    ret = 0.5 * (1 - ivy.cos(2.0 * ivy.pi * n / (M - 1)))\n    return ret\n\n\n@to_ivy_arrays_and_back\ndef kaiser(M, beta):\n    if M <= 1:\n        return ivy.ones([M], dtype=ivy.float64)\n    n = ivy.arange(M)\n    alpha = 0.5 * (M - 1)\n    ret = ivy.i0(beta * ivy.sqrt(1 - ((n - alpha) / alpha) ** 2)) / ivy.i0(beta)\n    return ret\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\ndef tri(N, M=None, k=0, dtype=\"float64\"):\n    if M is None:\n        M = N\n    ones = ivy.ones((N, M), dtype=dtype)\n    return ivy.tril(ones, k=k)\n\n\n@to_ivy_arrays_and_back\ndef blackman(M):\n    if M < 1:\n        return ivy.array([])\n    if M == 1:\n        return ivy.ones((1,))\n    n = ivy.arange(0, M)\n    alpha = 0.16\n    a0 = (1 - alpha) / 2\n    a1 = 1 / 2\n    a2 = alpha / 2\n    ret = (\n        a0\n        - a1 * ivy.cos(2 * ivy.pi * n / (M - 1))\n        + a2 * ivy.cos(4 * ivy.pi * n / (M - 1))\n    )\n    return ret\n", "path": "ivy/functional/frontends/jax/numpy/manipulations.py"}], "after_files": [{"content": "# local\nimport ivy\nfrom ivy.functional.frontends.jax.func_wrapper import (\n    to_ivy_arrays_and_back,\n    handle_jax_dtype,\n)\nfrom ivy.functional.frontends.jax.numpy import promote_types_of_jax_inputs\n\n\n@to_ivy_arrays_and_back\ndef clip(a, a_min=None, a_max=None, out=None):\n    ivy.utils.assertions.check_all_or_any_fn(\n        a_min,\n        a_max,\n        fn=ivy.exists,\n        type=\"any\",\n        limit=[1, 2],\n        message=\"at most one of a_min or a_max can be None\",\n    )\n    a = ivy.array(a)\n    if a_min is None:\n        a, a_max = promote_types_of_jax_inputs(a, a_max)\n        return ivy.minimum(a, a_max, out=out)\n    if a_max is None:\n        a, a_min = promote_types_of_jax_inputs(a, a_min)\n        return ivy.maximum(a, a_min, out=out)\n    return ivy.clip(a, a_min, a_max, out=out)\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\ndef concatenate(arrays, axis=0, dtype=None):\n    ret = ivy.concat(arrays, axis=axis)\n    if dtype:\n        ret = ivy.array(ret, dtype=dtype)\n    return ret\n\n\n@to_ivy_arrays_and_back\ndef repeat(a, repeats, axis=None, *, total_repeat_length=None):\n    return ivy.repeat(a, repeats, axis=axis)\n\n\n@to_ivy_arrays_and_back\ndef reshape(a, newshape, order=\"C\"):\n    return ivy.reshape(a, shape=newshape, order=order)\n\n\n@to_ivy_arrays_and_back\ndef ravel(a, order=\"C\"):\n    return ivy.reshape(a, shape=(-1,), order=order)\n\n\n@to_ivy_arrays_and_back\ndef resize(a, new_shape):\n    a = ivy.array(a)\n    resized_a = ivy.reshape(a, new_shape)\n    return resized_a\n\n\n@to_ivy_arrays_and_back\ndef moveaxis(a, source, destination):\n    return ivy.moveaxis(a, source, destination)\n\n\n@to_ivy_arrays_and_back\ndef flipud(m):\n    return ivy.flipud(m, out=None)\n\n\n@to_ivy_arrays_and_back\ndef transpose(a, axes=None):\n    if ivy.isscalar(a):\n        return ivy.array(a)\n    elif a.ndim == 1:\n        return a\n    if not axes:\n        axes = list(range(len(a.shape)))[::-1]\n    if type(axes) is int:\n        axes = [axes]\n    if (len(a.shape) == 0 and not axes) or (len(a.shape) == 1 and axes[0] == 0):\n        return a\n    return ivy.permute_dims(a, axes, out=None)\n\n\n@to_ivy_arrays_and_back\ndef flip(m, axis=None):\n    return ivy.flip(m, axis=axis)\n\n\n@to_ivy_arrays_and_back\ndef fliplr(m):\n    return ivy.fliplr(m)\n\n\n@to_ivy_arrays_and_back\ndef expand_dims(a, axis):\n    return ivy.expand_dims(a, axis=axis)\n\n\n@to_ivy_arrays_and_back\ndef stack(arrays, axis=0, out=None, dtype=None):\n    if dtype:\n        return ivy.astype(\n            ivy.stack(arrays, axis=axis, out=out), ivy.as_ivy_dtype(dtype)\n        )\n    return ivy.stack(arrays, axis=axis, out=out)\n\n\n@to_ivy_arrays_and_back\ndef take(\n    a,\n    indices,\n    axis=None,\n    out=None,\n    mode=None,\n    unique_indices=False,\n    indices_are_sorted=False,\n    fill_value=None,\n):\n    return ivy.gather(a, indices, axis=axis, out=out)\n\n\n@to_ivy_arrays_and_back\ndef broadcast_arrays(*args):\n    return ivy.broadcast_arrays(*args)\n\n\n@to_ivy_arrays_and_back\ndef broadcast_shapes(*shapes):\n    return ivy.broadcast_shapes(*shapes)\n\n\n@to_ivy_arrays_and_back\ndef broadcast_to(array, shape):\n    return ivy.broadcast_to(array, shape)\n\n\n@to_ivy_arrays_and_back\ndef append(arr, values, axis=None):\n    if axis is None:\n        return ivy.concat((ivy.flatten(arr), ivy.flatten(values)), axis=0)\n    else:\n        return ivy.concat((arr, values), axis=axis)\n\n\n@to_ivy_arrays_and_back\ndef swapaxes(a, axis1, axis2):\n    return ivy.swapaxes(a, axis1, axis2)\n\n\n@to_ivy_arrays_and_back\ndef atleast_3d(*arys):\n    return ivy.atleast_3d(*arys)\n\n\n@to_ivy_arrays_and_back\ndef atleast_1d(*arys):\n    return ivy.atleast_1d(*arys)\n\n\n@to_ivy_arrays_and_back\ndef atleast_2d(*arys):\n    return ivy.atleast_2d(*arys)\n\n\n@to_ivy_arrays_and_back\ndef tril(m, k=0):\n    return ivy.tril(m, k=k)\n\n\n@to_ivy_arrays_and_back\ndef block(arr):\n    # TODO: reimplement block\n    raise ivy.utils.exceptions.IvyNotImplementedError()\n\n\n@to_ivy_arrays_and_back\ndef squeeze(a, axis=None):\n    return ivy.squeeze(a, axis=axis)\n\n\n@to_ivy_arrays_and_back\ndef rot90(m, k=1, axes=(0, 1)):\n    return ivy.rot90(m, k=k, axes=axes)\n\n\n@to_ivy_arrays_and_back\ndef split(ary, indices_or_sections, axis=0):\n    if isinstance(indices_or_sections, (list, tuple, ivy.Array)):\n        indices_or_sections = (\n            ivy.diff(indices_or_sections, prepend=[0], append=[ary.shape[axis]])\n            .astype(ivy.int8)\n            .to_list()\n        )\n    return ivy.split(\n        ary, num_or_size_splits=indices_or_sections, axis=axis, with_remainder=False\n    )\n\n\n@to_ivy_arrays_and_back\ndef array_split(ary, indices_or_sections, axis=0):\n    return ivy.split(\n        ary, num_or_size_splits=indices_or_sections, axis=axis, with_remainder=True\n    )\n\n\n@to_ivy_arrays_and_back\ndef tile(A, reps):\n    return ivy.tile(A, reps)\n\n\n@to_ivy_arrays_and_back\ndef dsplit(ary, indices_or_sections):\n    if isinstance(indices_or_sections, (list, tuple, ivy.Array)):\n        indices_or_sections = (\n            ivy.diff(indices_or_sections, prepend=[0], append=[ary.shape[2]])\n            .astype(ivy.int8)\n            .to_list()\n        )\n    return ivy.dsplit(ary, indices_or_sections)\n\n\n@to_ivy_arrays_and_back\ndef dstack(tup, dtype=None):\n    return ivy.dstack(tup)\n\n\n@to_ivy_arrays_and_back\ndef vsplit(ary, indices_or_sections):\n    if isinstance(indices_or_sections, (list, tuple, ivy.Array)):\n        indices_or_sections = (\n            ivy.diff(indices_or_sections, prepend=[0], append=[ary.shape[0]])\n            .astype(ivy.int8)\n            .to_list()\n        )\n    return ivy.vsplit(ary, indices_or_sections)\n\n\n@to_ivy_arrays_and_back\ndef hsplit(ary, indices_or_sections):\n    if isinstance(indices_or_sections, (list, tuple, ivy.Array)):\n        if ary.ndim == 1:\n            indices_or_sections = (\n                ivy.diff(indices_or_sections, prepend=[0], append=[ary.shape[0]])\n                .astype(ivy.int8)\n                .to_list()\n            )\n        else:\n            indices_or_sections = (\n                ivy.diff(indices_or_sections, prepend=[0], append=[ary.shape[1]])\n                .astype(ivy.int8)\n                .to_list()\n            )\n    return ivy.hsplit(ary, indices_or_sections)\n\n\n@to_ivy_arrays_and_back\ndef roll(a, shift, axis=None):\n    return ivy.roll(a, shift, axis=axis)\n\n\n@to_ivy_arrays_and_back\ndef row_stack(tup):\n    if len(ivy.shape(tup[0])) == 1:\n        xs = []\n        for t in tup:\n            xs += [ivy.reshape(t, (1, ivy.shape(t)[0]))]\n        return ivy.concat(xs, axis=0)\n    return ivy.concat(tup, axis=0)\n\n\n@to_ivy_arrays_and_back\ndef column_stack(tup):\n    if len(ivy.shape(tup[0])) == 1:\n        ys = []\n        for t in tup:\n            ys += [ivy.reshape(t, (ivy.shape(t)[0], 1))]\n        return ivy.concat(ys, axis=1)\n    return ivy.concat(tup, axis=1)\n\n\n@to_ivy_arrays_and_back\ndef pad(array, pad_width, mode=\"constant\", **kwargs):\n    return ivy.pad(array, pad_width, mode=mode, **kwargs)\n\n\ndef hamming(M):\n    if M <= 1:\n        return ivy.ones([M], dtype=ivy.float64)\n    n = ivy.arange(M)\n    ret = 0.54 - 0.46 * ivy.cos(2.0 * ivy.pi * n / (M - 1))\n    return ret\n\n\n@to_ivy_arrays_and_back\ndef hanning(M):\n    if M <= 1:\n        return ivy.ones([M], dtype=ivy.float64)\n    n = ivy.arange(M)\n    ret = 0.5 * (1 - ivy.cos(2.0 * ivy.pi * n / (M - 1)))\n    return ret\n\n\n@to_ivy_arrays_and_back\ndef kaiser(M, beta):\n    if M <= 1:\n        return ivy.ones([M], dtype=ivy.float64)\n    n = ivy.arange(M)\n    alpha = 0.5 * (M - 1)\n    ret = ivy.i0(beta * ivy.sqrt(1 - ((n - alpha) / alpha) ** 2)) / ivy.i0(beta)\n    return ret\n\n\n@handle_jax_dtype\n@to_ivy_arrays_and_back\ndef tri(N, M=None, k=0, dtype=\"float64\"):\n    if M is None:\n        M = N\n    ones = ivy.ones((N, M), dtype=dtype)\n    return ivy.tril(ones, k=k)\n\n\n@to_ivy_arrays_and_back\ndef blackman(M):\n    if M < 1:\n        return ivy.array([])\n    if M == 1:\n        return ivy.ones((1,))\n    n = ivy.arange(0, M)\n    alpha = 0.16\n    a0 = (1 - alpha) / 2\n    a1 = 1 / 2\n    a2 = alpha / 2\n    ret = (\n        a0\n        - a1 * ivy.cos(2 * ivy.pi * n / (M - 1))\n        + a2 * ivy.cos(4 * ivy.pi * n / (M - 1))\n    )\n    return ret\n", "path": "ivy/functional/frontends/jax/numpy/manipulations.py"}]}