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


--- 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/tensor/creation.py`
Content:
```
1 # global
2 import ivy
3 from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
4 from .tensor import Tensor
5 from ivy.functional.frontends.paddle.func_wrapper import (
6     to_ivy_arrays_and_back,
7 )
8 
9 
10 @with_unsupported_dtypes({"2.5.1 and below": ("float16", "bfloat16")}, "paddle")
11 @to_ivy_arrays_and_back
12 def arange(start, end=None, step=1, dtype=None, name=None):
13     return ivy.arange(start, end, step=step, dtype=dtype)
14 
15 
16 @with_supported_dtypes(
17     {"2.5.1 and below": ("float16", "float32", "float64", "int32", "int64", "bool")},
18     "paddle",
19 )
20 @to_ivy_arrays_and_back
21 def assign(x, output=None):
22     if len(ivy.shape(x)) == 0:
23         x = ivy.reshape(ivy.Array(x), (1,))
24         if ivy.exists(output):
25             output = ivy.reshape(ivy.Array(output), (1,))
26     else:
27         x = ivy.reshape(x, ivy.shape(x))
28     ret = ivy.copy_array(x, to_ivy_array=False, out=output)
29     return ret
30 
31 
32 @with_supported_dtypes(
33     {"2.5.1 and below": ("float32", "float64")},
34     "paddle",
35 )
36 @to_ivy_arrays_and_back
37 def complex(real, imag, name=None):
38     assert real.dtype == imag.dtype, (
39         "(InvalidArgument) The type of data we are trying to retrieve does not match"
40         " the type of data currently contained in the container."
41     )
42     complex_dtype = "complex64" if real.dtype == "float32" else "complex128"
43     imag_cmplx = ivy.astype(imag, complex_dtype) * 1j
44     complex_array = real + imag_cmplx
45     return complex_array
46 
47 
48 @with_supported_dtypes(
49     {"2.5.1 and below": ("float32", "float64", "int32", "int64")}, "paddle"
50 )
51 @to_ivy_arrays_and_back
52 def diag(x, offset=0, padding_value=0, name=None):
53     if len(x.shape) == 1:
54         padding_value = ivy.astype(padding_value, ivy.dtype(x))
55         ret = ivy.diagflat(x, offset=offset, padding_value=padding_value)
56         if len(ret.shape) != 2:
57             ret = ivy.reshape(ret, (1, 1))
58     else:
59         ret = ivy.diag(x, k=offset)
60     return ret
61 
62 
63 @with_supported_dtypes(
64     {"2.5.1 and below": ("float32", "float64", "int32", "int64")}, "paddle"
65 )
66 @to_ivy_arrays_and_back
67 def diagflat(x, offset=0, name=None):
68     arr = ivy.diagflat(x, offset=offset)
69     return arr
70 
71 
72 @to_ivy_arrays_and_back
73 def empty(shape, dtype=None):
74     return ivy.empty(shape=shape, dtype=dtype)
75 
76 
77 @to_ivy_arrays_and_back
78 def empty_like(x, dtype=None, name=None):
79     return ivy.empty_like(x, dtype=dtype)
80 
81 
82 @to_ivy_arrays_and_back
83 def eye(num_rows, num_columns=None, dtype=None, name=None):
84     return ivy.eye(num_rows, num_columns, dtype=dtype)
85 
86 
87 @to_ivy_arrays_and_back
88 def full(shape, fill_value, /, *, dtype=None, name=None):
89     dtype = "float32" if dtype is None else dtype
90     return ivy.full(shape, fill_value, dtype=dtype)
91 
92 
93 @to_ivy_arrays_and_back
94 def full_like(x, fill_value, /, *, dtype=None, name=None):
95     dtype = x.dtype if dtype is None else dtype
96     return ivy.full_like(x, fill_value, dtype=dtype)
97 
98 
99 @with_supported_dtypes(
100     {"2.5.1 and below": ("float32", "float64", "int32", "int64")}, "paddle"
101 )
102 @to_ivy_arrays_and_back
103 def linspace(start, stop, num, dtype=None, name=None):
104     return ivy.linspace(start, stop, num=num, dtype=dtype)
105 
106 
107 @with_supported_dtypes(
108     {"2.5.1 and below": ("float32", "float64", "int32", "int64")}, "paddle"
109 )
110 @to_ivy_arrays_and_back
111 def logspace(start, stop, num, base=10.0, dtype=None, name=None):
112     return ivy.logspace(start, stop, num=num, base=base, dtype=dtype)
113 
114 
115 @with_supported_dtypes(
116     {"2.5.1 and below": ("float32", "float64", "int32", "int64")}, "paddle"
117 )
118 @to_ivy_arrays_and_back
119 def meshgrid(*args, **kwargs):
120     return ivy.meshgrid(*args, indexing="ij")
121 
122 
123 @with_unsupported_dtypes({"2.5.1 and below": "int8"}, "paddle")
124 @to_ivy_arrays_and_back
125 def ones(shape, /, *, dtype=None, name=None):
126     dtype = "float32" if dtype is None else dtype
127     return ivy.ones(shape, dtype=dtype)
128 
129 
130 @with_unsupported_dtypes(
131     {"2.5.1 and below": ("uint8", "int8", "complex64", "complex128")}, "paddle"
132 )
133 @to_ivy_arrays_and_back
134 def ones_like(x, /, *, dtype=None, name=None):
135     dtype = x.dtype if dtype is None else dtype
136     return ivy.ones_like(x, dtype=dtype)
137 
138 
139 @to_ivy_arrays_and_back
140 def to_tensor(data, /, *, dtype=None, place=None, stop_gradient=True):
141     array = ivy.array(data, dtype=dtype, device=place)
142     return Tensor(array, dtype=dtype, place=place)
143 
144 
145 @with_unsupported_dtypes(
146     {
147         "2.5.1 and below": (
148             "uint8",
149             "int8",
150             "int16",
151             "float16",
152             "complex64",
153             "complex128",
154             "bool",
155         )
156     },
157     "paddle",
158 )
159 @to_ivy_arrays_and_back
160 def tril(x, diagonal=0, name=None):
161     return ivy.tril(x, k=diagonal)
162 
163 
164 @with_supported_dtypes({"2.5.1 and below": ("int32", "int64")}, "paddle")
165 @to_ivy_arrays_and_back
166 def tril_indices(row, col, offset=0, dtype="int64"):
167     arr = ivy.tril_indices(row, col, offset)
168     arr = ivy.astype(arr, dtype)
169     return arr
170 
171 
172 @with_unsupported_dtypes(
173     {
174         "2.5.1 and below": (
175             "uint8",
176             "int8",
177             "int16",
178             "float16",
179             "complex64",
180             "complex128",
181             "bool",
182         )
183     },
184     "paddle",
185 )
186 @to_ivy_arrays_and_back
187 def triu(x, diagonal=0, name=None):
188     return ivy.triu(x, k=diagonal)
189 
190 
191 @with_supported_dtypes({"2.5.1 and below": ("int32", "int64")}, "paddle")
192 @to_ivy_arrays_and_back
193 def triu_indices(row, col=None, offset=0, dtype="int64"):
194     arr = ivy.triu_indices(row, col, offset)
195     if not ivy.to_scalar(ivy.shape(arr[0], as_array=True)):
196         return arr
197     arr = ivy.astype(arr, dtype)
198     return arr
199 
200 
201 @with_unsupported_dtypes({"2.5.1 and below": "int8"}, "paddle")
202 @to_ivy_arrays_and_back
203 def zeros(shape, /, *, dtype=None, name=None):
204     dtype = "float32" if dtype is None else dtype
205     return ivy.zeros(shape, dtype=dtype)
206 
207 
208 @with_unsupported_dtypes(
209     {"2.5.1 and below": ("uint8", "int8", "complex64", "complex128")}, "paddle"
210 )
211 @to_ivy_arrays_and_back
212 def zeros_like(x, /, *, dtype=None, name=None):
213     dtype = x.dtype if dtype is None else dtype
214     return ivy.zeros_like(x, dtype=dtype)
215
```
--- 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/tensor/creation.py b/ivy/functional/frontends/paddle/tensor/creation.py
--- a/ivy/functional/frontends/paddle/tensor/creation.py
+++ b/ivy/functional/frontends/paddle/tensor/creation.py
@@ -29,6 +29,14 @@
     return ret
 
 
+@with_unsupported_dtypes(
+    {"2.5.1 and below": ("bfloat16", "uint16", "uint32", "uint64")}, "paddle"
+)
+@to_ivy_arrays_and_back
+def clone(x):
+    return ivy.copy_array(x)
+
+
 @with_supported_dtypes(
     {"2.5.1 and below": ("float32", "float64")},
     "paddle",

{"golden_diff": "diff --git a/ivy/functional/frontends/paddle/tensor/creation.py b/ivy/functional/frontends/paddle/tensor/creation.py\n--- a/ivy/functional/frontends/paddle/tensor/creation.py\n+++ b/ivy/functional/frontends/paddle/tensor/creation.py\n@@ -29,6 +29,14 @@\n     return ret\n \n \n+@with_unsupported_dtypes(\n+    {\"2.5.1 and below\": (\"bfloat16\", \"uint16\", \"uint32\", \"uint64\")}, \"paddle\"\n+)\n+@to_ivy_arrays_and_back\n+def clone(x):\n+    return ivy.copy_array(x)\n+\n+\n @with_supported_dtypes(\n     {\"2.5.1 and below\": (\"float32\", \"float64\")},\n     \"paddle\",\n", "issue": "clone\n\n", "before_files": [{"content": "# global\nimport ivy\nfrom ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes\nfrom .tensor import Tensor\nfrom ivy.functional.frontends.paddle.func_wrapper import (\n    to_ivy_arrays_and_back,\n)\n\n\n@with_unsupported_dtypes({\"2.5.1 and below\": (\"float16\", \"bfloat16\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef arange(start, end=None, step=1, dtype=None, name=None):\n    return ivy.arange(start, end, step=step, dtype=dtype)\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float16\", \"float32\", \"float64\", \"int32\", \"int64\", \"bool\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef assign(x, output=None):\n    if len(ivy.shape(x)) == 0:\n        x = ivy.reshape(ivy.Array(x), (1,))\n        if ivy.exists(output):\n            output = ivy.reshape(ivy.Array(output), (1,))\n    else:\n        x = ivy.reshape(x, ivy.shape(x))\n    ret = ivy.copy_array(x, to_ivy_array=False, out=output)\n    return ret\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef complex(real, imag, name=None):\n    assert real.dtype == imag.dtype, (\n        \"(InvalidArgument) The type of data we are trying to retrieve does not match\"\n        \" the type of data currently contained in the container.\"\n    )\n    complex_dtype = \"complex64\" if real.dtype == \"float32\" else \"complex128\"\n    imag_cmplx = ivy.astype(imag, complex_dtype) * 1j\n    complex_array = real + imag_cmplx\n    return complex_array\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\", \"int32\", \"int64\")}, \"paddle\"\n)\n@to_ivy_arrays_and_back\ndef diag(x, offset=0, padding_value=0, name=None):\n    if len(x.shape) == 1:\n        padding_value = ivy.astype(padding_value, ivy.dtype(x))\n        ret = ivy.diagflat(x, offset=offset, padding_value=padding_value)\n        if len(ret.shape) != 2:\n            ret = ivy.reshape(ret, (1, 1))\n    else:\n        ret = ivy.diag(x, k=offset)\n    return ret\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\", \"int32\", \"int64\")}, \"paddle\"\n)\n@to_ivy_arrays_and_back\ndef diagflat(x, offset=0, name=None):\n    arr = ivy.diagflat(x, offset=offset)\n    return arr\n\n\n@to_ivy_arrays_and_back\ndef empty(shape, dtype=None):\n    return ivy.empty(shape=shape, dtype=dtype)\n\n\n@to_ivy_arrays_and_back\ndef empty_like(x, dtype=None, name=None):\n    return ivy.empty_like(x, dtype=dtype)\n\n\n@to_ivy_arrays_and_back\ndef eye(num_rows, num_columns=None, dtype=None, name=None):\n    return ivy.eye(num_rows, num_columns, dtype=dtype)\n\n\n@to_ivy_arrays_and_back\ndef full(shape, fill_value, /, *, dtype=None, name=None):\n    dtype = \"float32\" if dtype is None else dtype\n    return ivy.full(shape, fill_value, dtype=dtype)\n\n\n@to_ivy_arrays_and_back\ndef full_like(x, fill_value, /, *, dtype=None, name=None):\n    dtype = x.dtype if dtype is None else dtype\n    return ivy.full_like(x, fill_value, dtype=dtype)\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\", \"int32\", \"int64\")}, \"paddle\"\n)\n@to_ivy_arrays_and_back\ndef linspace(start, stop, num, dtype=None, name=None):\n    return ivy.linspace(start, stop, num=num, dtype=dtype)\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\", \"int32\", \"int64\")}, \"paddle\"\n)\n@to_ivy_arrays_and_back\ndef logspace(start, stop, num, base=10.0, dtype=None, name=None):\n    return ivy.logspace(start, stop, num=num, base=base, dtype=dtype)\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\", \"int32\", \"int64\")}, \"paddle\"\n)\n@to_ivy_arrays_and_back\ndef meshgrid(*args, **kwargs):\n    return ivy.meshgrid(*args, indexing=\"ij\")\n\n\n@with_unsupported_dtypes({\"2.5.1 and below\": \"int8\"}, \"paddle\")\n@to_ivy_arrays_and_back\ndef ones(shape, /, *, dtype=None, name=None):\n    dtype = \"float32\" if dtype is None else dtype\n    return ivy.ones(shape, dtype=dtype)\n\n\n@with_unsupported_dtypes(\n    {\"2.5.1 and below\": (\"uint8\", \"int8\", \"complex64\", \"complex128\")}, \"paddle\"\n)\n@to_ivy_arrays_and_back\ndef ones_like(x, /, *, dtype=None, name=None):\n    dtype = x.dtype if dtype is None else dtype\n    return ivy.ones_like(x, dtype=dtype)\n\n\n@to_ivy_arrays_and_back\ndef to_tensor(data, /, *, dtype=None, place=None, stop_gradient=True):\n    array = ivy.array(data, dtype=dtype, device=place)\n    return Tensor(array, dtype=dtype, place=place)\n\n\n@with_unsupported_dtypes(\n    {\n        \"2.5.1 and below\": (\n            \"uint8\",\n            \"int8\",\n            \"int16\",\n            \"float16\",\n            \"complex64\",\n            \"complex128\",\n            \"bool\",\n        )\n    },\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef tril(x, diagonal=0, name=None):\n    return ivy.tril(x, k=diagonal)\n\n\n@with_supported_dtypes({\"2.5.1 and below\": (\"int32\", \"int64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef tril_indices(row, col, offset=0, dtype=\"int64\"):\n    arr = ivy.tril_indices(row, col, offset)\n    arr = ivy.astype(arr, dtype)\n    return arr\n\n\n@with_unsupported_dtypes(\n    {\n        \"2.5.1 and below\": (\n            \"uint8\",\n            \"int8\",\n            \"int16\",\n            \"float16\",\n            \"complex64\",\n            \"complex128\",\n            \"bool\",\n        )\n    },\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef triu(x, diagonal=0, name=None):\n    return ivy.triu(x, k=diagonal)\n\n\n@with_supported_dtypes({\"2.5.1 and below\": (\"int32\", \"int64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef triu_indices(row, col=None, offset=0, dtype=\"int64\"):\n    arr = ivy.triu_indices(row, col, offset)\n    if not ivy.to_scalar(ivy.shape(arr[0], as_array=True)):\n        return arr\n    arr = ivy.astype(arr, dtype)\n    return arr\n\n\n@with_unsupported_dtypes({\"2.5.1 and below\": \"int8\"}, \"paddle\")\n@to_ivy_arrays_and_back\ndef zeros(shape, /, *, dtype=None, name=None):\n    dtype = \"float32\" if dtype is None else dtype\n    return ivy.zeros(shape, dtype=dtype)\n\n\n@with_unsupported_dtypes(\n    {\"2.5.1 and below\": (\"uint8\", \"int8\", \"complex64\", \"complex128\")}, \"paddle\"\n)\n@to_ivy_arrays_and_back\ndef zeros_like(x, /, *, dtype=None, name=None):\n    dtype = x.dtype if dtype is None else dtype\n    return ivy.zeros_like(x, dtype=dtype)\n", "path": "ivy/functional/frontends/paddle/tensor/creation.py"}], "after_files": [{"content": "# global\nimport ivy\nfrom ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes\nfrom .tensor import Tensor\nfrom ivy.functional.frontends.paddle.func_wrapper import (\n    to_ivy_arrays_and_back,\n)\n\n\n@with_unsupported_dtypes({\"2.5.1 and below\": (\"float16\", \"bfloat16\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef arange(start, end=None, step=1, dtype=None, name=None):\n    return ivy.arange(start, end, step=step, dtype=dtype)\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float16\", \"float32\", \"float64\", \"int32\", \"int64\", \"bool\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef assign(x, output=None):\n    if len(ivy.shape(x)) == 0:\n        x = ivy.reshape(ivy.Array(x), (1,))\n        if ivy.exists(output):\n            output = ivy.reshape(ivy.Array(output), (1,))\n    else:\n        x = ivy.reshape(x, ivy.shape(x))\n    ret = ivy.copy_array(x, to_ivy_array=False, out=output)\n    return ret\n\n\n@with_unsupported_dtypes(\n    {\"2.5.1 and below\": (\"bfloat16\", \"uint16\", \"uint32\", \"uint64\")}, \"paddle\"\n)\n@to_ivy_arrays_and_back\ndef clone(x):\n    return ivy.copy_array(x)\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef complex(real, imag, name=None):\n    assert real.dtype == imag.dtype, (\n        \"(InvalidArgument) The type of data we are trying to retrieve does not match\"\n        \" the type of data currently contained in the container.\"\n    )\n    complex_dtype = \"complex64\" if real.dtype == \"float32\" else \"complex128\"\n    imag_cmplx = ivy.astype(imag, complex_dtype) * 1j\n    complex_array = real + imag_cmplx\n    return complex_array\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\", \"int32\", \"int64\")}, \"paddle\"\n)\n@to_ivy_arrays_and_back\ndef diag(x, offset=0, padding_value=0, name=None):\n    if len(x.shape) == 1:\n        padding_value = ivy.astype(padding_value, ivy.dtype(x))\n        ret = ivy.diagflat(x, offset=offset, padding_value=padding_value)\n        if len(ret.shape) != 2:\n            ret = ivy.reshape(ret, (1, 1))\n    else:\n        ret = ivy.diag(x, k=offset)\n    return ret\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\", \"int32\", \"int64\")}, \"paddle\"\n)\n@to_ivy_arrays_and_back\ndef diagflat(x, offset=0, name=None):\n    arr = ivy.diagflat(x, offset=offset)\n    return arr\n\n\n@to_ivy_arrays_and_back\ndef empty(shape, dtype=None):\n    return ivy.empty(shape=shape, dtype=dtype)\n\n\n@to_ivy_arrays_and_back\ndef empty_like(x, dtype=None, name=None):\n    return ivy.empty_like(x, dtype=dtype)\n\n\n@to_ivy_arrays_and_back\ndef eye(num_rows, num_columns=None, dtype=None, name=None):\n    return ivy.eye(num_rows, num_columns, dtype=dtype)\n\n\n@to_ivy_arrays_and_back\ndef full(shape, fill_value, /, *, dtype=None, name=None):\n    dtype = \"float32\" if dtype is None else dtype\n    return ivy.full(shape, fill_value, dtype=dtype)\n\n\n@to_ivy_arrays_and_back\ndef full_like(x, fill_value, /, *, dtype=None, name=None):\n    dtype = x.dtype if dtype is None else dtype\n    return ivy.full_like(x, fill_value, dtype=dtype)\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\", \"int32\", \"int64\")}, \"paddle\"\n)\n@to_ivy_arrays_and_back\ndef linspace(start, stop, num, dtype=None, name=None):\n    return ivy.linspace(start, stop, num=num, dtype=dtype)\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\", \"int32\", \"int64\")}, \"paddle\"\n)\n@to_ivy_arrays_and_back\ndef logspace(start, stop, num, base=10.0, dtype=None, name=None):\n    return ivy.logspace(start, stop, num=num, base=base, dtype=dtype)\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\", \"int32\", \"int64\")}, \"paddle\"\n)\n@to_ivy_arrays_and_back\ndef meshgrid(*args, **kwargs):\n    return ivy.meshgrid(*args, indexing=\"ij\")\n\n\n@with_unsupported_dtypes({\"2.5.1 and below\": \"int8\"}, \"paddle\")\n@to_ivy_arrays_and_back\ndef ones(shape, /, *, dtype=None, name=None):\n    dtype = \"float32\" if dtype is None else dtype\n    return ivy.ones(shape, dtype=dtype)\n\n\n@with_unsupported_dtypes(\n    {\"2.5.1 and below\": (\"uint8\", \"int8\", \"complex64\", \"complex128\")}, \"paddle\"\n)\n@to_ivy_arrays_and_back\ndef ones_like(x, /, *, dtype=None, name=None):\n    dtype = x.dtype if dtype is None else dtype\n    return ivy.ones_like(x, dtype=dtype)\n\n\n@to_ivy_arrays_and_back\ndef to_tensor(data, /, *, dtype=None, place=None, stop_gradient=True):\n    array = ivy.array(data, dtype=dtype, device=place)\n    return Tensor(array, dtype=dtype, place=place)\n\n\n@with_unsupported_dtypes(\n    {\n        \"2.5.1 and below\": (\n            \"uint8\",\n            \"int8\",\n            \"int16\",\n            \"float16\",\n            \"complex64\",\n            \"complex128\",\n            \"bool\",\n        )\n    },\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef tril(x, diagonal=0, name=None):\n    return ivy.tril(x, k=diagonal)\n\n\n@with_supported_dtypes({\"2.5.1 and below\": (\"int32\", \"int64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef tril_indices(row, col, offset=0, dtype=\"int64\"):\n    arr = ivy.tril_indices(row, col, offset)\n    arr = ivy.astype(arr, dtype)\n    return arr\n\n\n@with_unsupported_dtypes(\n    {\n        \"2.5.1 and below\": (\n            \"uint8\",\n            \"int8\",\n            \"int16\",\n            \"float16\",\n            \"complex64\",\n            \"complex128\",\n            \"bool\",\n        )\n    },\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef triu(x, diagonal=0, name=None):\n    return ivy.triu(x, k=diagonal)\n\n\n@with_supported_dtypes({\"2.5.1 and below\": (\"int32\", \"int64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef triu_indices(row, col=None, offset=0, dtype=\"int64\"):\n    arr = ivy.triu_indices(row, col, offset)\n    if not ivy.to_scalar(ivy.shape(arr[0], as_array=True)):\n        return arr\n    arr = ivy.astype(arr, dtype)\n    return arr\n\n\n@with_unsupported_dtypes({\"2.5.1 and below\": \"int8\"}, \"paddle\")\n@to_ivy_arrays_and_back\ndef zeros(shape, /, *, dtype=None, name=None):\n    dtype = \"float32\" if dtype is None else dtype\n    return ivy.zeros(shape, dtype=dtype)\n\n\n@with_unsupported_dtypes(\n    {\"2.5.1 and below\": (\"uint8\", \"int8\", \"complex64\", \"complex128\")}, \"paddle\"\n)\n@to_ivy_arrays_and_back\ndef zeros_like(x, /, *, dtype=None, name=None):\n    dtype = x.dtype if dtype is None else dtype\n    return ivy.zeros_like(x, dtype=dtype)\n", "path": "ivy/functional/frontends/paddle/tensor/creation.py"}]}