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


--- 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/linalg.py`
Content:
```
1 # global
2 import ivy
3 from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
4 from ivy.functional.frontends.paddle import promote_types_of_paddle_inputs
5 from ivy.functional.frontends.paddle.func_wrapper import (
6     to_ivy_arrays_and_back,
7 )
8 
9 
10 @with_supported_dtypes(
11     {"2.4.2 and below": ("float32", "float64", "int32", "int64")}, "paddle"
12 )
13 @to_ivy_arrays_and_back
14 def cross(x, y, /, *, axis=9, name=None):
15     x, y = promote_types_of_paddle_inputs(x, y)
16     return ivy.cross(x, y, axis=axis)
17 
18 
19 # matmul
20 @with_unsupported_dtypes({"2.4.2 and below": ("float16", "bfloat16")}, "paddle")
21 @to_ivy_arrays_and_back
22 def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
23     x, y = promote_types_of_paddle_inputs(x, y)
24     return ivy.matmul(x, y, transpose_a=transpose_x, transpose_b=transpose_y)
25 
26 
27 # norm
28 @with_supported_dtypes({"2.4.2 and below": ("float32", "float64")}, "paddle")
29 @to_ivy_arrays_and_back
30 def norm(x, p="fro", axis=None, keepdim=False, name=None):
31     if axis is None and p is not None:
32         if p == "fro":
33             p = 2
34         ret = ivy.vector_norm(x.flatten(), ord=p, axis=-1)
35         if keepdim:
36             ret = ret.reshape([1] * len(x.shape))
37         if len(ret.shape) == 0:
38             return ivy.array([ret])
39         return ret
40 
41     if isinstance(axis, tuple):
42         axis = list(axis)
43     if isinstance(axis, list) and len(axis) == 1:
44         axis = axis[0]
45 
46     if isinstance(axis, int):
47         if p == "fro":
48             p = 2
49         if p in [0, 1, 2, ivy.inf, -ivy.inf]:
50             ret = ivy.vector_norm(x, ord=p, axis=axis, keepdims=keepdim)
51         elif isinstance(p, (int, float)):
52             ret = ivy.pow(
53                 ivy.sum(ivy.pow(ivy.abs(x), p), axis=axis, keepdims=keepdim),
54                 float(1.0 / p),
55             )
56 
57     elif isinstance(axis, list) and len(axis) == 2:
58         if p == 0:
59             raise ValueError
60         elif p == 1:
61             ret = ivy.sum(ivy.abs(x), axis=axis, keepdims=keepdim)
62         elif p == 2 or p == "fro":
63             ret = ivy.matrix_norm(x, ord="fro", axis=axis, keepdims=keepdim)
64         elif p == ivy.inf:
65             ret = ivy.max(ivy.abs(x), axis=axis, keepdims=keepdim)
66         elif p == -ivy.inf:
67             ret = ivy.min(ivy.abs(x), axis=axis, keepdims=keepdim)
68         elif isinstance(p, (int, float)) and p > 0:
69             ret = ivy.pow(
70                 ivy.sum(ivy.pow(ivy.abs(x), p), axis=axis, keepdims=keepdim),
71                 float(1.0 / p),
72             )
73         else:
74             raise ValueError
75 
76     else:
77         raise ValueError
78 
79     if len(ret.shape) == 0:
80         ret = ivy.array(
81             [ret]
82         )  # this is done so as to match shape of output from paddle
83     return ret
84 
85 
86 # eig
87 @to_ivy_arrays_and_back
88 def eig(x, name=None):
89     return ivy.eig(x)
90 
91 
92 # eigvals
93 @to_ivy_arrays_and_back
94 def eigvals(x, name=None):
95     return ivy.eigvals(x)
96 
97 
98 # eigvalsh
99 @to_ivy_arrays_and_back
100 def eigvalsh(x, UPLO="L", name=None):
101     return ivy.eigvalsh(x, UPLO=UPLO)
102 
103 
104 # eigh
105 @to_ivy_arrays_and_back
106 def eigh(x, UPLO="L", name=None):
107     return ivy.eigh(x, UPLO=UPLO)
108 
109 
110 # pinv
111 @with_unsupported_dtypes({"2.4.2 and below": ("float16", "bfloat16")}, "paddle")
112 @to_ivy_arrays_and_back
113 def pinv(x, rcond=1e-15, hermitian=False, name=None):
114     # TODO: Add hermitian functionality
115     return ivy.pinv(x, rtol=rcond)
116 
117 
118 # solve
119 @with_unsupported_dtypes({"2.4.2 and below": ("float16", "bfloat16")}, "paddle")
120 @to_ivy_arrays_and_back
121 def solve(x1, x2, name=None):
122     return ivy.solve(x1, x2)
123 
124 
125 # cholesky
126 @with_supported_dtypes({"2.4.2 and below": ("float32", "float64")}, "paddle")
127 @to_ivy_arrays_and_back
128 def cholesky(x, /, *, upper=False, name=None):
129     return ivy.cholesky(x, upper=upper)
130 
131 
132 # bmm
133 @with_unsupported_dtypes({"2.4.2 and below": ("float16", "bfloat16")}, "paddle")
134 @to_ivy_arrays_and_back
135 def bmm(x, y, transpose_x=False, transpose_y=False, name=None):
136     if len(ivy.shape(x)) != 3 or len(ivy.shape(y)) != 3:
137         raise RuntimeError("input must be 3D matrices")
138     x, y = promote_types_of_paddle_inputs(x, y)
139     return ivy.matmul(x, y, transpose_a=transpose_x, transpose_b=transpose_y)
140 
141 
142 # matrix_power
143 @with_unsupported_dtypes({"2.4.2 and below": ("float16", "bfloat16")}, "paddle")
144 @to_ivy_arrays_and_back
145 def matrix_power(x, n, name=None):
146     return ivy.matrix_power(x, n)
147
```
--- 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/linalg.py b/ivy/functional/frontends/paddle/tensor/linalg.py
--- a/ivy/functional/frontends/paddle/tensor/linalg.py
+++ b/ivy/functional/frontends/paddle/tensor/linalg.py
@@ -130,3 +130,10 @@
         raise RuntimeError("input must be 3D matrices")
     x, y = promote_types_of_paddle_inputs(x, y)
     return ivy.matmul(x, y, transpose_a=transpose_x, transpose_b=transpose_y)
+
+
+# matrix_power
+@with_unsupported_dtypes({"2.4.2 and below": ("float16", "bfloat16")}, "paddle")
+@to_ivy_arrays_and_back
+def matrix_power(x, n, name=None):
+    return ivy.matrix_power(x, n)

{"golden_diff": "diff --git a/ivy/functional/frontends/paddle/tensor/linalg.py b/ivy/functional/frontends/paddle/tensor/linalg.py\n--- a/ivy/functional/frontends/paddle/tensor/linalg.py\n+++ b/ivy/functional/frontends/paddle/tensor/linalg.py\n@@ -130,3 +130,10 @@\n         raise RuntimeError(\"input must be 3D matrices\")\n     x, y = promote_types_of_paddle_inputs(x, y)\n     return ivy.matmul(x, y, transpose_a=transpose_x, transpose_b=transpose_y)\n+\n+\n+# matrix_power\n+@with_unsupported_dtypes({\"2.4.2 and below\": (\"float16\", \"bfloat16\")}, \"paddle\")\n+@to_ivy_arrays_and_back\n+def matrix_power(x, n, name=None):\n+    return ivy.matrix_power(x, n)\n", "issue": " matrix_power\n\n", "before_files": [{"content": "# global\nimport ivy\nfrom ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes\nfrom ivy.functional.frontends.paddle import promote_types_of_paddle_inputs\nfrom ivy.functional.frontends.paddle.func_wrapper import (\n    to_ivy_arrays_and_back,\n)\n\n\n@with_supported_dtypes(\n    {\"2.4.2 and below\": (\"float32\", \"float64\", \"int32\", \"int64\")}, \"paddle\"\n)\n@to_ivy_arrays_and_back\ndef cross(x, y, /, *, axis=9, name=None):\n    x, y = promote_types_of_paddle_inputs(x, y)\n    return ivy.cross(x, y, axis=axis)\n\n\n# matmul\n@with_unsupported_dtypes({\"2.4.2 and below\": (\"float16\", \"bfloat16\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef matmul(x, y, transpose_x=False, transpose_y=False, name=None):\n    x, y = promote_types_of_paddle_inputs(x, y)\n    return ivy.matmul(x, y, transpose_a=transpose_x, transpose_b=transpose_y)\n\n\n# norm\n@with_supported_dtypes({\"2.4.2 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef norm(x, p=\"fro\", axis=None, keepdim=False, name=None):\n    if axis is None and p is not None:\n        if p == \"fro\":\n            p = 2\n        ret = ivy.vector_norm(x.flatten(), ord=p, axis=-1)\n        if keepdim:\n            ret = ret.reshape([1] * len(x.shape))\n        if len(ret.shape) == 0:\n            return ivy.array([ret])\n        return ret\n\n    if isinstance(axis, tuple):\n        axis = list(axis)\n    if isinstance(axis, list) and len(axis) == 1:\n        axis = axis[0]\n\n    if isinstance(axis, int):\n        if p == \"fro\":\n            p = 2\n        if p in [0, 1, 2, ivy.inf, -ivy.inf]:\n            ret = ivy.vector_norm(x, ord=p, axis=axis, keepdims=keepdim)\n        elif isinstance(p, (int, float)):\n            ret = ivy.pow(\n                ivy.sum(ivy.pow(ivy.abs(x), p), axis=axis, keepdims=keepdim),\n                float(1.0 / p),\n            )\n\n    elif isinstance(axis, list) and len(axis) == 2:\n        if p == 0:\n            raise ValueError\n        elif p == 1:\n            ret = ivy.sum(ivy.abs(x), axis=axis, keepdims=keepdim)\n        elif p == 2 or p == \"fro\":\n            ret = ivy.matrix_norm(x, ord=\"fro\", axis=axis, keepdims=keepdim)\n        elif p == ivy.inf:\n            ret = ivy.max(ivy.abs(x), axis=axis, keepdims=keepdim)\n        elif p == -ivy.inf:\n            ret = ivy.min(ivy.abs(x), axis=axis, keepdims=keepdim)\n        elif isinstance(p, (int, float)) and p > 0:\n            ret = ivy.pow(\n                ivy.sum(ivy.pow(ivy.abs(x), p), axis=axis, keepdims=keepdim),\n                float(1.0 / p),\n            )\n        else:\n            raise ValueError\n\n    else:\n        raise ValueError\n\n    if len(ret.shape) == 0:\n        ret = ivy.array(\n            [ret]\n        )  # this is done so as to match shape of output from paddle\n    return ret\n\n\n# eig\n@to_ivy_arrays_and_back\ndef eig(x, name=None):\n    return ivy.eig(x)\n\n\n# eigvals\n@to_ivy_arrays_and_back\ndef eigvals(x, name=None):\n    return ivy.eigvals(x)\n\n\n# eigvalsh\n@to_ivy_arrays_and_back\ndef eigvalsh(x, UPLO=\"L\", name=None):\n    return ivy.eigvalsh(x, UPLO=UPLO)\n\n\n# eigh\n@to_ivy_arrays_and_back\ndef eigh(x, UPLO=\"L\", name=None):\n    return ivy.eigh(x, UPLO=UPLO)\n\n\n# pinv\n@with_unsupported_dtypes({\"2.4.2 and below\": (\"float16\", \"bfloat16\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef pinv(x, rcond=1e-15, hermitian=False, name=None):\n    # TODO: Add hermitian functionality\n    return ivy.pinv(x, rtol=rcond)\n\n\n# solve\n@with_unsupported_dtypes({\"2.4.2 and below\": (\"float16\", \"bfloat16\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef solve(x1, x2, name=None):\n    return ivy.solve(x1, x2)\n\n\n# cholesky\n@with_supported_dtypes({\"2.4.2 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef cholesky(x, /, *, upper=False, name=None):\n    return ivy.cholesky(x, upper=upper)\n\n\n# bmm\n@with_unsupported_dtypes({\"2.4.2 and below\": (\"float16\", \"bfloat16\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef bmm(x, y, transpose_x=False, transpose_y=False, name=None):\n    if len(ivy.shape(x)) != 3 or len(ivy.shape(y)) != 3:\n        raise RuntimeError(\"input must be 3D matrices\")\n    x, y = promote_types_of_paddle_inputs(x, y)\n    return ivy.matmul(x, y, transpose_a=transpose_x, transpose_b=transpose_y)\n\n\n# matrix_power\n@with_unsupported_dtypes({\"2.4.2 and below\": (\"float16\", \"bfloat16\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef matrix_power(x, n, name=None):\n    return ivy.matrix_power(x, n)\n", "path": "ivy/functional/frontends/paddle/tensor/linalg.py"}], "after_files": [{"content": "# global\nimport ivy\nfrom ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes\nfrom ivy.functional.frontends.paddle import promote_types_of_paddle_inputs\nfrom ivy.functional.frontends.paddle.func_wrapper import (\n    to_ivy_arrays_and_back,\n)\n\n\n@with_supported_dtypes(\n    {\"2.4.2 and below\": (\"float32\", \"float64\", \"int32\", \"int64\")}, \"paddle\"\n)\n@to_ivy_arrays_and_back\ndef cross(x, y, /, *, axis=9, name=None):\n    x, y = promote_types_of_paddle_inputs(x, y)\n    return ivy.cross(x, y, axis=axis)\n\n\n# matmul\n@with_unsupported_dtypes({\"2.4.2 and below\": (\"float16\", \"bfloat16\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef matmul(x, y, transpose_x=False, transpose_y=False, name=None):\n    x, y = promote_types_of_paddle_inputs(x, y)\n    return ivy.matmul(x, y, transpose_a=transpose_x, transpose_b=transpose_y)\n\n\n# norm\n@with_supported_dtypes({\"2.4.2 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef norm(x, p=\"fro\", axis=None, keepdim=False, name=None):\n    if axis is None and p is not None:\n        if p == \"fro\":\n            p = 2\n        ret = ivy.vector_norm(x.flatten(), ord=p, axis=-1)\n        if keepdim:\n            ret = ret.reshape([1] * len(x.shape))\n        if len(ret.shape) == 0:\n            return ivy.array([ret])\n        return ret\n\n    if isinstance(axis, tuple):\n        axis = list(axis)\n    if isinstance(axis, list) and len(axis) == 1:\n        axis = axis[0]\n\n    if isinstance(axis, int):\n        if p == \"fro\":\n            p = 2\n        if p in [0, 1, 2, ivy.inf, -ivy.inf]:\n            ret = ivy.vector_norm(x, ord=p, axis=axis, keepdims=keepdim)\n        elif isinstance(p, (int, float)):\n            ret = ivy.pow(\n                ivy.sum(ivy.pow(ivy.abs(x), p), axis=axis, keepdims=keepdim),\n                float(1.0 / p),\n            )\n\n    elif isinstance(axis, list) and len(axis) == 2:\n        if p == 0:\n            raise ValueError\n        elif p == 1:\n            ret = ivy.sum(ivy.abs(x), axis=axis, keepdims=keepdim)\n        elif p == 2 or p == \"fro\":\n            ret = ivy.matrix_norm(x, ord=\"fro\", axis=axis, keepdims=keepdim)\n        elif p == ivy.inf:\n            ret = ivy.max(ivy.abs(x), axis=axis, keepdims=keepdim)\n        elif p == -ivy.inf:\n            ret = ivy.min(ivy.abs(x), axis=axis, keepdims=keepdim)\n        elif isinstance(p, (int, float)) and p > 0:\n            ret = ivy.pow(\n                ivy.sum(ivy.pow(ivy.abs(x), p), axis=axis, keepdims=keepdim),\n                float(1.0 / p),\n            )\n        else:\n            raise ValueError\n\n    else:\n        raise ValueError\n\n    if len(ret.shape) == 0:\n        ret = ivy.array(\n            [ret]\n        )  # this is done so as to match shape of output from paddle\n    return ret\n\n\n# eig\n@to_ivy_arrays_and_back\ndef eig(x, name=None):\n    return ivy.eig(x)\n\n\n# eigvals\n@to_ivy_arrays_and_back\ndef eigvals(x, name=None):\n    return ivy.eigvals(x)\n\n\n# eigvalsh\n@to_ivy_arrays_and_back\ndef eigvalsh(x, UPLO=\"L\", name=None):\n    return ivy.eigvalsh(x, UPLO=UPLO)\n\n\n# eigh\n@to_ivy_arrays_and_back\ndef eigh(x, UPLO=\"L\", name=None):\n    return ivy.eigh(x, UPLO=UPLO)\n\n\n# pinv\n@with_unsupported_dtypes({\"2.4.2 and below\": (\"float16\", \"bfloat16\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef pinv(x, rcond=1e-15, hermitian=False, name=None):\n    # TODO: Add hermitian functionality\n    return ivy.pinv(x, rtol=rcond)\n\n\n# cholesky\n@with_supported_dtypes({\"2.4.2 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef cholesky(x, /, *, upper=False, name=None):\n    return ivy.cholesky(x, upper=upper)\n\n\n# bmm\n@with_unsupported_dtypes({\"2.4.2 and below\": (\"float16\", \"bfloat16\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef bmm(x, y, transpose_x=False, transpose_y=False, name=None):\n    if len(ivy.shape(x)) != 3 or len(ivy.shape(y)) != 3:\n        raise RuntimeError(\"input must be 3D matrices\")\n    x, y = promote_types_of_paddle_inputs(x, y)\n    return ivy.matmul(x, y, transpose_a=transpose_x, transpose_b=transpose_y)\n\n\n# matrix_power\n@with_unsupported_dtypes({\"2.4.2 and below\": (\"float16\", \"bfloat16\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef matrix_power(x, n, name=None):\n    return ivy.matrix_power(x, n)\n", "path": "ivy/functional/frontends/paddle/tensor/linalg.py"}]}