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


--- 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 # matmul
11 @with_unsupported_dtypes({"2.4.2 and below": ("float16", "bfloat16")}, "paddle")
12 @to_ivy_arrays_and_back
13 def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
14     x, y = promote_types_of_paddle_inputs(x, y)
15     return ivy.matmul(x, y, transpose_a=transpose_x, transpose_b=transpose_y)
16 
17 
18 # norm
19 @with_supported_dtypes({"2.4.2 and below": ("float32", "float64")}, "paddle")
20 @to_ivy_arrays_and_back
21 def norm(x, p="fro", axis=None, keepdim=False, name=None):
22     if axis is None and p is not None:
23         if p == "fro":
24             p = 2
25         ret = ivy.vector_norm(x.flatten(), ord=p, axis=-1)
26         if keepdim:
27             ret = ret.reshape([1] * len(x.shape))
28         if len(ret.shape) == 0:
29             return ivy.array([ret])
30         return ret
31 
32     if isinstance(axis, tuple):
33         axis = list(axis)
34     if isinstance(axis, list) and len(axis) == 1:
35         axis = axis[0]
36 
37     if isinstance(axis, int):
38         if p == "fro":
39             p = 2
40         if p in [0, 1, 2, ivy.inf, -ivy.inf]:
41             ret = ivy.vector_norm(x, ord=p, axis=axis, keepdims=keepdim)
42         elif isinstance(p, (int, float)):
43             ret = ivy.pow(
44                 ivy.sum(ivy.pow(ivy.abs(x), p), axis=axis, keepdims=keepdim),
45                 float(1.0 / p),
46             )
47 
48     elif isinstance(axis, list) and len(axis) == 2:
49         if p == 0:
50             raise ValueError
51         elif p == 1:
52             ret = ivy.sum(ivy.abs(x), axis=axis, keepdims=keepdim)
53         elif p == 2 or p == "fro":
54             ret = ivy.matrix_norm(x, ord="fro", axis=axis, keepdims=keepdim)
55         elif p == ivy.inf:
56             ret = ivy.max(ivy.abs(x), axis=axis, keepdims=keepdim)
57         elif p == -ivy.inf:
58             ret = ivy.min(ivy.abs(x), axis=axis, keepdims=keepdim)
59         elif isinstance(p, (int, float)) and p > 0:
60             ret = ivy.pow(
61                 ivy.sum(ivy.pow(ivy.abs(x), p), axis=axis, keepdims=keepdim),
62                 float(1.0 / p),
63             )
64         else:
65             raise ValueError
66 
67     else:
68         raise ValueError
69 
70     if len(ret.shape) == 0:
71         ret = ivy.array(
72             [ret]
73         )  # this is done so as to match shape of output from paddle
74     return ret
75 
76 
77 # eig
78 @to_ivy_arrays_and_back
79 def eig(x, name=None):
80     return ivy.eig(x)
81 
82 
83 # eigvals
84 @to_ivy_arrays_and_back
85 def eigvals(x, name=None):
86     return ivy.eigvals(x)
87 
88 
89 # eigvalsh
90 @to_ivy_arrays_and_back
91 def eigvalsh(x, UPLO="L", name=None):
92     return ivy.eigvalsh(x, UPLO=UPLO)
93 
94 
95 # eigh
96 @to_ivy_arrays_and_back
97 def eigh(x, UPLO="L", name=None):
98     return ivy.eigh(x, UPLO=UPLO)
99 
100 
101 # pinv
102 @with_unsupported_dtypes({"2.4.2 and below": ("float16", "bfloat16")}, "paddle")
103 @to_ivy_arrays_and_back
104 def pinv(x, rcond=1e-15, hermitian=False, name=None):
105     # TODO: Add hermitian functionality
106     return ivy.pinv(x, rtol=rcond)
107
```
--- 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
@@ -7,6 +7,15 @@
 )
 
 
+@with_supported_dtypes(
+    {"2.4.2 and below": ("float32", "float64", "int32", "int64")}, "paddle"
+)
+@to_ivy_arrays_and_back
+def cross(x, y, /, *, axis=9, name=None):
+    x, y = promote_types_of_paddle_inputs(x, y)
+    return ivy.cross(x, y, axis=axis)
+
+
 # matmul
 @with_unsupported_dtypes({"2.4.2 and below": ("float16", "bfloat16")}, "paddle")
 @to_ivy_arrays_and_back

{"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@@ -7,6 +7,15 @@\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\n+def 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\n", "issue": "cross\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# 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", "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", "path": "ivy/functional/frontends/paddle/tensor/linalg.py"}]}