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


--- 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/paddle/manipulation.py`
Content:
```
1 # global
2 import ivy
3 from ivy.functional.frontends.paddle.func_wrapper import (
4     to_ivy_arrays_and_back,
5 )
6 from ivy.func_wrapper import (
7     with_unsupported_dtypes,
8     with_supported_dtypes,
9     with_supported_device_and_dtypes,
10 )
11 
12 
13 @with_unsupported_dtypes({"2.6.0 and below": ("float16", "bfloat16")}, "paddle")
14 @to_ivy_arrays_and_back
15 def abs(x, name=None):
16     return ivy.abs(x)
17 
18 
19 @with_supported_dtypes(
20     {"2.6.0 and below": ("bool", "float32", "float64", "int32", "int64")},
21     "paddle",
22 )
23 @to_ivy_arrays_and_back
24 def broadcast_to(x, shape, name=None):
25     return ivy.broadcast_to(x, shape)
26 
27 
28 @with_supported_dtypes(
29     {
30         "2.6.0 and below": (
31             "bool",
32             "float16",
33             "float32",
34             "float64",
35             "int32",
36             "int64",
37             "uint8",
38         )
39     },
40     "paddle",
41 )
42 @to_ivy_arrays_and_back
43 def cast(x, dtype):
44     return ivy.astype(x, dtype)
45 
46 
47 @with_unsupported_dtypes({"2.6.0 and below": ("int8", "int16")}, "paddle")
48 @to_ivy_arrays_and_back
49 def concat(x, axis, name=None):
50     return ivy.concat(x, axis=axis)
51 
52 
53 @with_supported_dtypes(
54     {"2.6.0 and below": ("bool", "float32", "float64", "int32", "int64")},
55     "paddle",
56 )
57 @to_ivy_arrays_and_back
58 def expand(x, shape, name=None):
59     return ivy.expand(x, shape)
60 
61 
62 @with_unsupported_dtypes(
63     {"2.6.0 and below": ("int8", "uint8", "int16", "float16")},
64     "paddle",
65 )
66 @to_ivy_arrays_and_back
67 def flip(x, axis, name=None):
68     return ivy.flip(x, axis=axis)
69 
70 
71 @with_supported_dtypes(
72     {"2.6.0 and below": ("bool", "float32", "float64", "int32", "int64")},
73     "paddle",
74 )
75 @to_ivy_arrays_and_back
76 def gather(params, indices, axis=-1, batch_dims=0, name=None):
77     return ivy.gather(params, indices, axis=axis, batch_dims=batch_dims)
78 
79 
80 @with_unsupported_dtypes(
81     {"2.6.0 and below": ("int8", "uint8", "int16", "uint16", "float16", "bfloat16")},
82     "paddle",
83 )
84 @to_ivy_arrays_and_back
85 def gather_nd(x, index, name=None):
86     return ivy.gather_nd(x, index)
87 
88 
89 @with_supported_dtypes(
90     {"2.6.0 and below": ("bool", "int32", "int64", "float16", "float32", "float64")},
91     "paddle",
92 )
93 @to_ivy_arrays_and_back
94 def index_add(x, index, axis, value, *, name=None):
95     x = ivy.swapaxes(x, axis, 0)
96     value = ivy.swapaxes(value, axis, 0)
97     _to_adds = []
98     index = sorted(zip(ivy.to_list(index), range(len(index))), key=(lambda i: i[0]))
99     while index:
100         _curr_idx = index[0][0]
101         while len(_to_adds) < _curr_idx:
102             _to_adds.append(ivy.zeros_like(value[0]))
103         _to_add_cum = ivy.get_item(value, index[0][1])
104         while (len(index)) > 1 and (index[0][0] == index[1][0]):
105             _to_add_cum = _to_add_cum + ivy.get_item(value, index.pop(1)[1])
106         index.pop(0)
107         _to_adds.append(_to_add_cum)
108     while len(_to_adds) < x.shape[0]:
109         _to_adds.append(ivy.zeros_like(value[0]))
110     _to_adds = ivy.stack(_to_adds)
111     if len(x.shape) < 2:
112         # Added this line due to the paddle backend treating scalars as 1-d arrays
113         _to_adds = ivy.flatten(_to_adds)
114 
115     ret = ivy.add(x, _to_adds)
116     ret = ivy.swapaxes(ret, axis, 0)
117     return ret
118 
119 
120 @to_ivy_arrays_and_back
121 def put_along_axis(arr, indices, values, axis, reduce="assign"):
122     result = ivy.put_along_axis(arr, indices, values, axis)
123     return result
124 
125 
126 @with_supported_dtypes(
127     {"2.6.0 and below": ("int32", "int64", "float32", "float64")},
128     "paddle",
129 )
130 @to_ivy_arrays_and_back
131 def repeat_interleave(x, repeats, axis=None, name=None):
132     return ivy.repeat(x, repeats, axis=axis)
133 
134 
135 @to_ivy_arrays_and_back
136 def reshape(x, shape, name=None):
137     return ivy.reshape(x, shape)
138 
139 
140 @with_supported_dtypes(
141     {
142         "2.5.0 and below": (
143             "float32",
144             "float64",
145             "int32",
146             "int64",
147             "complex64",
148             "complex128",
149         )
150     },
151     "paddle",
152 )
153 @to_ivy_arrays_and_back
154 def roll(x, shifts, axis=None, name=None):
155     return ivy.roll(x, shifts, axis=axis)
156 
157 
158 @with_supported_device_and_dtypes(
159     {
160         "2.6.0 and above": {
161             "cpu": (
162                 "bool",
163                 "int32",
164                 "int64",
165                 "float32",
166                 "float64",
167             ),
168             "gpu": ("float16",),
169         },
170     },
171     "paddle",
172 )
173 @to_ivy_arrays_and_back
174 def rot90(x, k=1, axes=(0, 1), name=None):
175     return ivy.rot90(x, k=k, axes=axes)
176 
177 
178 @with_unsupported_dtypes(
179     {"2.6.0 and below": ("int16", "complex64", "complex128")},
180     "paddle",
181 )
182 @to_ivy_arrays_and_back
183 def split(x, num_or_sections, axis=0, name=None):
184     return ivy.split(x, num_or_size_splits=num_or_sections, axis=axis)
185 
186 
187 @with_unsupported_dtypes(
188     {"2.6.0 and below": ("float16", "bfloat16", "int8", "int16")},
189     "paddle",
190 )
191 @to_ivy_arrays_and_back
192 def squeeze(x, axis=None, name=None):
193     return ivy.squeeze(x, axis=axis)
194 
195 
196 @to_ivy_arrays_and_back
197 def stack(x, axis=0, name=None):
198     return ivy.stack(x, axis=axis)
199 
200 
201 @with_supported_dtypes(
202     {"2.6.0 and below": ("float32", "float64")},
203     "paddle",
204 )
205 @to_ivy_arrays_and_back
206 def take_along_axis(arr, indices, axis):
207     return ivy.take_along_axis(arr, indices, axis)
208 
209 
210 @with_unsupported_dtypes(
211     {"2.6.0 and below": ("int8", "uint8", "int16", "float16")},
212     "paddle",
213 )
214 @to_ivy_arrays_and_back
215 def tile(x, repeat_times, name=None):
216     return ivy.tile(x, repeats=repeat_times)
217 
218 
219 @to_ivy_arrays_and_back
220 def tolist(x):
221     return ivy.to_list(x)
222 
223 
224 @with_supported_dtypes(
225     {"2.6.0 and below": ("bool", "int32", "int64", "float16", "float32", "float64")},
226     "paddle",
227 )
228 @to_ivy_arrays_and_back
229 def unbind(input, axis=0):
230     shape = list(input.shape)
231     num_splits = shape[axis]
232     shape.pop(axis)
233     return tuple(x.reshape(tuple(shape)) for x in split(input, num_splits, axis=axis))
234 
235 
236 @with_supported_dtypes(
237     {"2.6.0 and below": ("bool", "int32", "int64", "float16", "float32", "float64")},
238     "paddle",
239 )
240 @to_ivy_arrays_and_back
241 def unique_consecutive(x, axis=0):
242     return ivy.unique_consecutive(x, axis=axis)
243 
244 
245 @with_supported_dtypes(
246     {
247         "2.6.0 and below": (
248             "float32",
249             "float64",
250             "int32",
251             "int64",
252         )
253     },
254     "paddle",
255 )
256 @to_ivy_arrays_and_back
257 def unstack(x, axis=0, name=None):
258     return ivy.unstack(x, axis=axis)
259 
260 
261 absolute = abs
262
```
--- 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/paddle/manipulation.py b/ivy/functional/frontends/paddle/manipulation.py
--- a/ivy/functional/frontends/paddle/manipulation.py
+++ b/ivy/functional/frontends/paddle/manipulation.py
@@ -117,6 +117,16 @@
     return ret
 
 
+@with_supported_dtypes({"2.5.1 and below": ("int32", "int64")}, "paddle")
+@to_ivy_arrays_and_back
+def one_hot(x, num_classes, name=None):
+    if not isinstance(num_classes, int) or num_classes <= 0:
+        raise ValueError("num_classes must be a positive integer.")
+
+    one_hot_tensor = ivy.one_hot(x, num_classes)
+    return one_hot_tensor.astype(ivy.float32)
+
+
 @to_ivy_arrays_and_back
 def put_along_axis(arr, indices, values, axis, reduce="assign"):
     result = ivy.put_along_axis(arr, indices, values, axis)

{"golden_diff": "diff --git a/ivy/functional/frontends/paddle/manipulation.py b/ivy/functional/frontends/paddle/manipulation.py\n--- a/ivy/functional/frontends/paddle/manipulation.py\n+++ b/ivy/functional/frontends/paddle/manipulation.py\n@@ -117,6 +117,16 @@\n     return ret\n \n \n+@with_supported_dtypes({\"2.5.1 and below\": (\"int32\", \"int64\")}, \"paddle\")\n+@to_ivy_arrays_and_back\n+def one_hot(x, num_classes, name=None):\n+    if not isinstance(num_classes, int) or num_classes <= 0:\n+        raise ValueError(\"num_classes must be a positive integer.\")\n+\n+    one_hot_tensor = ivy.one_hot(x, num_classes)\n+    return one_hot_tensor.astype(ivy.float32)\n+\n+\n @to_ivy_arrays_and_back\n def put_along_axis(arr, indices, values, axis, reduce=\"assign\"):\n     result = ivy.put_along_axis(arr, indices, values, axis)\n", "issue": "one_hot\n\n", "before_files": [{"content": "# global\nimport ivy\nfrom ivy.functional.frontends.paddle.func_wrapper import (\n    to_ivy_arrays_and_back,\n)\nfrom ivy.func_wrapper import (\n    with_unsupported_dtypes,\n    with_supported_dtypes,\n    with_supported_device_and_dtypes,\n)\n\n\n@with_unsupported_dtypes({\"2.6.0 and below\": (\"float16\", \"bfloat16\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef abs(x, name=None):\n    return ivy.abs(x)\n\n\n@with_supported_dtypes(\n    {\"2.6.0 and below\": (\"bool\", \"float32\", \"float64\", \"int32\", \"int64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef broadcast_to(x, shape, name=None):\n    return ivy.broadcast_to(x, shape)\n\n\n@with_supported_dtypes(\n    {\n        \"2.6.0 and below\": (\n            \"bool\",\n            \"float16\",\n            \"float32\",\n            \"float64\",\n            \"int32\",\n            \"int64\",\n            \"uint8\",\n        )\n    },\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef cast(x, dtype):\n    return ivy.astype(x, dtype)\n\n\n@with_unsupported_dtypes({\"2.6.0 and below\": (\"int8\", \"int16\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef concat(x, axis, name=None):\n    return ivy.concat(x, axis=axis)\n\n\n@with_supported_dtypes(\n    {\"2.6.0 and below\": (\"bool\", \"float32\", \"float64\", \"int32\", \"int64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef expand(x, shape, name=None):\n    return ivy.expand(x, shape)\n\n\n@with_unsupported_dtypes(\n    {\"2.6.0 and below\": (\"int8\", \"uint8\", \"int16\", \"float16\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef flip(x, axis, name=None):\n    return ivy.flip(x, axis=axis)\n\n\n@with_supported_dtypes(\n    {\"2.6.0 and below\": (\"bool\", \"float32\", \"float64\", \"int32\", \"int64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef gather(params, indices, axis=-1, batch_dims=0, name=None):\n    return ivy.gather(params, indices, axis=axis, batch_dims=batch_dims)\n\n\n@with_unsupported_dtypes(\n    {\"2.6.0 and below\": (\"int8\", \"uint8\", \"int16\", \"uint16\", \"float16\", \"bfloat16\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef gather_nd(x, index, name=None):\n    return ivy.gather_nd(x, index)\n\n\n@with_supported_dtypes(\n    {\"2.6.0 and below\": (\"bool\", \"int32\", \"int64\", \"float16\", \"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef index_add(x, index, axis, value, *, name=None):\n    x = ivy.swapaxes(x, axis, 0)\n    value = ivy.swapaxes(value, axis, 0)\n    _to_adds = []\n    index = sorted(zip(ivy.to_list(index), range(len(index))), key=(lambda i: i[0]))\n    while index:\n        _curr_idx = index[0][0]\n        while len(_to_adds) < _curr_idx:\n            _to_adds.append(ivy.zeros_like(value[0]))\n        _to_add_cum = ivy.get_item(value, index[0][1])\n        while (len(index)) > 1 and (index[0][0] == index[1][0]):\n            _to_add_cum = _to_add_cum + ivy.get_item(value, index.pop(1)[1])\n        index.pop(0)\n        _to_adds.append(_to_add_cum)\n    while len(_to_adds) < x.shape[0]:\n        _to_adds.append(ivy.zeros_like(value[0]))\n    _to_adds = ivy.stack(_to_adds)\n    if len(x.shape) < 2:\n        # Added this line due to the paddle backend treating scalars as 1-d arrays\n        _to_adds = ivy.flatten(_to_adds)\n\n    ret = ivy.add(x, _to_adds)\n    ret = ivy.swapaxes(ret, axis, 0)\n    return ret\n\n\n@to_ivy_arrays_and_back\ndef put_along_axis(arr, indices, values, axis, reduce=\"assign\"):\n    result = ivy.put_along_axis(arr, indices, values, axis)\n    return result\n\n\n@with_supported_dtypes(\n    {\"2.6.0 and below\": (\"int32\", \"int64\", \"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef repeat_interleave(x, repeats, axis=None, name=None):\n    return ivy.repeat(x, repeats, axis=axis)\n\n\n@to_ivy_arrays_and_back\ndef reshape(x, shape, name=None):\n    return ivy.reshape(x, shape)\n\n\n@with_supported_dtypes(\n    {\n        \"2.5.0 and below\": (\n            \"float32\",\n            \"float64\",\n            \"int32\",\n            \"int64\",\n            \"complex64\",\n            \"complex128\",\n        )\n    },\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef roll(x, shifts, axis=None, name=None):\n    return ivy.roll(x, shifts, axis=axis)\n\n\n@with_supported_device_and_dtypes(\n    {\n        \"2.6.0 and above\": {\n            \"cpu\": (\n                \"bool\",\n                \"int32\",\n                \"int64\",\n                \"float32\",\n                \"float64\",\n            ),\n            \"gpu\": (\"float16\",),\n        },\n    },\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef rot90(x, k=1, axes=(0, 1), name=None):\n    return ivy.rot90(x, k=k, axes=axes)\n\n\n@with_unsupported_dtypes(\n    {\"2.6.0 and below\": (\"int16\", \"complex64\", \"complex128\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef split(x, num_or_sections, axis=0, name=None):\n    return ivy.split(x, num_or_size_splits=num_or_sections, axis=axis)\n\n\n@with_unsupported_dtypes(\n    {\"2.6.0 and below\": (\"float16\", \"bfloat16\", \"int8\", \"int16\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef squeeze(x, axis=None, name=None):\n    return ivy.squeeze(x, axis=axis)\n\n\n@to_ivy_arrays_and_back\ndef stack(x, axis=0, name=None):\n    return ivy.stack(x, axis=axis)\n\n\n@with_supported_dtypes(\n    {\"2.6.0 and below\": (\"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef take_along_axis(arr, indices, axis):\n    return ivy.take_along_axis(arr, indices, axis)\n\n\n@with_unsupported_dtypes(\n    {\"2.6.0 and below\": (\"int8\", \"uint8\", \"int16\", \"float16\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef tile(x, repeat_times, name=None):\n    return ivy.tile(x, repeats=repeat_times)\n\n\n@to_ivy_arrays_and_back\ndef tolist(x):\n    return ivy.to_list(x)\n\n\n@with_supported_dtypes(\n    {\"2.6.0 and below\": (\"bool\", \"int32\", \"int64\", \"float16\", \"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef unbind(input, axis=0):\n    shape = list(input.shape)\n    num_splits = shape[axis]\n    shape.pop(axis)\n    return tuple(x.reshape(tuple(shape)) for x in split(input, num_splits, axis=axis))\n\n\n@with_supported_dtypes(\n    {\"2.6.0 and below\": (\"bool\", \"int32\", \"int64\", \"float16\", \"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef unique_consecutive(x, axis=0):\n    return ivy.unique_consecutive(x, axis=axis)\n\n\n@with_supported_dtypes(\n    {\n        \"2.6.0 and below\": (\n            \"float32\",\n            \"float64\",\n            \"int32\",\n            \"int64\",\n        )\n    },\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef unstack(x, axis=0, name=None):\n    return ivy.unstack(x, axis=axis)\n\n\nabsolute = abs\n", "path": "ivy/functional/frontends/paddle/manipulation.py"}], "after_files": [{"content": "# global\nimport ivy\nfrom ivy.functional.frontends.paddle.func_wrapper import (\n    to_ivy_arrays_and_back,\n)\nfrom ivy.func_wrapper import (\n    with_unsupported_dtypes,\n    with_supported_dtypes,\n    with_supported_device_and_dtypes,\n)\n\n\n@with_unsupported_dtypes({\"2.6.0 and below\": (\"float16\", \"bfloat16\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef abs(x, name=None):\n    return ivy.abs(x)\n\n\n@with_supported_dtypes(\n    {\"2.6.0 and below\": (\"bool\", \"float32\", \"float64\", \"int32\", \"int64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef broadcast_to(x, shape, name=None):\n    return ivy.broadcast_to(x, shape)\n\n\n@with_supported_dtypes(\n    {\n        \"2.6.0 and below\": (\n            \"bool\",\n            \"float16\",\n            \"float32\",\n            \"float64\",\n            \"int32\",\n            \"int64\",\n            \"uint8\",\n        )\n    },\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef cast(x, dtype):\n    return ivy.astype(x, dtype)\n\n\n@with_unsupported_dtypes({\"2.6.0 and below\": (\"int8\", \"int16\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef concat(x, axis, name=None):\n    return ivy.concat(x, axis=axis)\n\n\n@with_supported_dtypes(\n    {\"2.6.0 and below\": (\"bool\", \"float32\", \"float64\", \"int32\", \"int64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef expand(x, shape, name=None):\n    return ivy.expand(x, shape)\n\n\n@with_unsupported_dtypes(\n    {\"2.6.0 and below\": (\"int8\", \"uint8\", \"int16\", \"float16\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef flip(x, axis, name=None):\n    return ivy.flip(x, axis=axis)\n\n\n@with_supported_dtypes(\n    {\"2.6.0 and below\": (\"bool\", \"float32\", \"float64\", \"int32\", \"int64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef gather(params, indices, axis=-1, batch_dims=0, name=None):\n    return ivy.gather(params, indices, axis=axis, batch_dims=batch_dims)\n\n\n@with_unsupported_dtypes(\n    {\"2.6.0 and below\": (\"int8\", \"uint8\", \"int16\", \"uint16\", \"float16\", \"bfloat16\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef gather_nd(x, index, name=None):\n    return ivy.gather_nd(x, index)\n\n\n@with_supported_dtypes(\n    {\"2.6.0 and below\": (\"bool\", \"int32\", \"int64\", \"float16\", \"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef index_add(x, index, axis, value, *, name=None):\n    x = ivy.swapaxes(x, axis, 0)\n    value = ivy.swapaxes(value, axis, 0)\n    _to_adds = []\n    index = sorted(zip(ivy.to_list(index), range(len(index))), key=(lambda i: i[0]))\n    while index:\n        _curr_idx = index[0][0]\n        while len(_to_adds) < _curr_idx:\n            _to_adds.append(ivy.zeros_like(value[0]))\n        _to_add_cum = ivy.get_item(value, index[0][1])\n        while (len(index)) > 1 and (index[0][0] == index[1][0]):\n            _to_add_cum = _to_add_cum + ivy.get_item(value, index.pop(1)[1])\n        index.pop(0)\n        _to_adds.append(_to_add_cum)\n    while len(_to_adds) < x.shape[0]:\n        _to_adds.append(ivy.zeros_like(value[0]))\n    _to_adds = ivy.stack(_to_adds)\n    if len(x.shape) < 2:\n        # Added this line due to the paddle backend treating scalars as 1-d arrays\n        _to_adds = ivy.flatten(_to_adds)\n\n    ret = ivy.add(x, _to_adds)\n    ret = ivy.swapaxes(ret, axis, 0)\n    return ret\n\n\n@with_supported_dtypes({\"2.5.1 and below\": (\"int32\", \"int64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef one_hot(x, num_classes, name=None):\n    if not isinstance(num_classes, int) or num_classes <= 0:\n        raise ValueError(\"num_classes must be a positive integer.\")\n\n    one_hot_tensor = ivy.one_hot(x, num_classes)\n    return one_hot_tensor.astype(ivy.float32)\n\n\n@to_ivy_arrays_and_back\ndef put_along_axis(arr, indices, values, axis, reduce=\"assign\"):\n    result = ivy.put_along_axis(arr, indices, values, axis)\n    return result\n\n\n@with_supported_dtypes(\n    {\"2.6.0 and below\": (\"int32\", \"int64\", \"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef repeat_interleave(x, repeats, axis=None, name=None):\n    return ivy.repeat(x, repeats, axis=axis)\n\n\n@to_ivy_arrays_and_back\ndef reshape(x, shape, name=None):\n    return ivy.reshape(x, shape)\n\n\n@with_supported_dtypes(\n    {\n        \"2.5.0 and below\": (\n            \"float32\",\n            \"float64\",\n            \"int32\",\n            \"int64\",\n            \"complex64\",\n            \"complex128\",\n        )\n    },\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef roll(x, shifts, axis=None, name=None):\n    return ivy.roll(x, shifts, axis=axis)\n\n\n@with_supported_device_and_dtypes(\n    {\n        \"2.6.0 and above\": {\n            \"cpu\": (\n                \"bool\",\n                \"int32\",\n                \"int64\",\n                \"float32\",\n                \"float64\",\n            ),\n            \"gpu\": (\"float16\",),\n        },\n    },\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef rot90(x, k=1, axes=(0, 1), name=None):\n    return ivy.rot90(x, k=k, axes=axes)\n\n\n@with_unsupported_dtypes(\n    {\"2.6.0 and below\": (\"int16\", \"complex64\", \"complex128\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef split(x, num_or_sections, axis=0, name=None):\n    return ivy.split(x, num_or_size_splits=num_or_sections, axis=axis)\n\n\n@with_unsupported_dtypes(\n    {\"2.6.0 and below\": (\"float16\", \"bfloat16\", \"int8\", \"int16\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef squeeze(x, axis=None, name=None):\n    return ivy.squeeze(x, axis=axis)\n\n\n@to_ivy_arrays_and_back\ndef stack(x, axis=0, name=None):\n    return ivy.stack(x, axis=axis)\n\n\n@with_supported_dtypes(\n    {\"2.6.0 and below\": (\"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef take_along_axis(arr, indices, axis):\n    return ivy.take_along_axis(arr, indices, axis)\n\n\n@with_unsupported_dtypes(\n    {\"2.6.0 and below\": (\"int8\", \"uint8\", \"int16\", \"float16\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef tile(x, repeat_times, name=None):\n    return ivy.tile(x, repeats=repeat_times)\n\n\n@to_ivy_arrays_and_back\ndef tolist(x):\n    return ivy.to_list(x)\n\n\n@with_supported_dtypes(\n    {\"2.6.0 and below\": (\"bool\", \"int32\", \"int64\", \"float16\", \"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef unbind(input, axis=0):\n    shape = list(input.shape)\n    num_splits = shape[axis]\n    shape.pop(axis)\n    return tuple(x.reshape(tuple(shape)) for x in split(input, num_splits, axis=axis))\n\n\n@with_supported_dtypes(\n    {\"2.6.0 and below\": (\"bool\", \"int32\", \"int64\", \"float16\", \"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef unique_consecutive(x, axis=0):\n    return ivy.unique_consecutive(x, axis=axis)\n\n\n@with_supported_dtypes(\n    {\n        \"2.6.0 and below\": (\n            \"float32\",\n            \"float64\",\n            \"int32\",\n            \"int64\",\n        )\n    },\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef unstack(x, axis=0, name=None):\n    return ivy.unstack(x, axis=axis)\n\n\nabsolute = abs\n", "path": "ivy/functional/frontends/paddle/manipulation.py"}]}