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


--- 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/random.py`
Content:
```
1 # global
2 import ivy
3 from ivy.func_wrapper import with_supported_dtypes
4 from ivy.func_wrapper import with_supported_device_and_dtypes, with_unsupported_dtypes
5 from ivy.functional.frontends.paddle.func_wrapper import (
6     to_ivy_arrays_and_back,
7 )
8 
9 
10 @with_supported_dtypes(
11     {"2.5.1 and below": ("float32", "float64")},
12     "paddle",
13 )
14 @to_ivy_arrays_and_back
15 def normal(mean=0.0, std=1.0, shape=None, name=None):
16     return ivy.random_normal(mean=mean, std=std, shape=shape)
17 
18 
19 @with_supported_dtypes(
20     {"2.5.1 and below": ("float32", "float64")},
21     "paddle",
22 )
23 @to_ivy_arrays_and_back
24 def poisson(x, name=None):
25     return ivy.poisson(x, shape=None, device=None, dtype=None, seed=None, out=None)
26 
27 
28 @with_supported_device_and_dtypes(
29     {
30         "2.5.1 and above": {
31             "cpu": (
32                 "bfloat16",
33                 "float32",
34                 "float64",
35             ),
36             "gpu": (
37                 "bfloat16",
38                 "float16",
39                 "float32",
40                 "float64",
41             ),
42         },
43         "2.4.2 and below": {
44             "cpu": (
45                 "float32",
46                 "float64",
47             ),
48             "gpu": (
49                 "float16",
50                 "float32",
51                 "float64",
52             ),
53         },
54     },
55     "paddle",
56 )
57 @to_ivy_arrays_and_back
58 def rand(shape, dtype=None, name=None):
59     return ivy.random_uniform(low=0.0, high=1.0, shape=shape, dtype=dtype, seed=None)
60 
61 
62 @to_ivy_arrays_and_back
63 def randint(low=0, high=None, shape=[1], dtype=None, name=None):
64     return ivy.randint(low, high, shape=shape, dtype=dtype)
65 
66 
67 @with_unsupported_dtypes(
68     {"2.5.1 and below": ("int16", "float16", "bfloat16", "uint8")},
69     "paddle",
70 )
71 @to_ivy_arrays_and_back
72 def randint_like(x, low=0, high=None, dtype=None, name=None):
73     if high is None:
74         high = low
75         low = 0
76         if high <= 0:
77             raise ivy.exceptions.IvyError(
78                 "If high is None, low must be greater than 0, but received low = 0."
79             )
80     return ivy.randint(low, high, shape=x.shape, dtype=dtype, seed=None)
81 
82 
83 def randn(shape, dtype=None, name=None):
84     if dtype not in ["float32", "float64"]:
85         raise ivy.exceptions.IvyError(
86             "Unsupported dtype for randn, only float32 and float64 are supported, "
87         )
88     return ivy.random_normal(shape=shape, dtype=dtype, seed=None)
89 
90 
91 @with_supported_dtypes(
92     {"2.5.1 and below": ("float32", "float64")},
93     "paddle",
94 )
95 @to_ivy_arrays_and_back
96 def standard_normal(shape, dtype=None, name=None):
97     return ivy.random_normal(mean=0, std=1, shape=shape, dtype=dtype)
98 
99 
100 @with_supported_dtypes(
101     {"2.5.1 and below": ("float32", "float64")},
102     "paddle",
103 )
104 @to_ivy_arrays_and_back
105 def uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None):
106     return ivy.random_uniform(low=min, high=max, shape=shape, dtype=dtype, seed=seed)
107 
108 
109 @with_supported_dtypes(
110     {"2.5.1 and below": ("float32", "float64")},
111     "paddle",
112 )
113 @to_ivy_arrays_and_back
114 def uniform_(x, min=-1.0, max=1.0, seed=0, name=None):
115     x = ivy.array(x)
116     return ivy.random_uniform(
117         low=min, high=max, shape=x.shape, dtype=x.dtype, seed=seed
118     )
119
```
--- 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/random.py b/ivy/functional/frontends/paddle/tensor/random.py
--- a/ivy/functional/frontends/paddle/tensor/random.py
+++ b/ivy/functional/frontends/paddle/tensor/random.py
@@ -7,6 +7,15 @@
 )
 
 
+@with_supported_dtypes(
+    {"2.5.1 and below": ("float32", "float64")},
+    "paddle",
+)
+@to_ivy_arrays_and_back
+def exponential_(x, lam=1.0, name=None):
+    return ivy.multiply(lam, ivy.exp(ivy.multiply(-lam, x)))
+
+
 @with_supported_dtypes(
     {"2.5.1 and below": ("float32", "float64")},
     "paddle",

{"golden_diff": "diff --git a/ivy/functional/frontends/paddle/tensor/random.py b/ivy/functional/frontends/paddle/tensor/random.py\n--- a/ivy/functional/frontends/paddle/tensor/random.py\n+++ b/ivy/functional/frontends/paddle/tensor/random.py\n@@ -7,6 +7,15 @@\n )\n \n \n+@with_supported_dtypes(\n+    {\"2.5.1 and below\": (\"float32\", \"float64\")},\n+    \"paddle\",\n+)\n+@to_ivy_arrays_and_back\n+def exponential_(x, lam=1.0, name=None):\n+    return ivy.multiply(lam, ivy.exp(ivy.multiply(-lam, x)))\n+\n+\n @with_supported_dtypes(\n     {\"2.5.1 and below\": (\"float32\", \"float64\")},\n     \"paddle\",\n", "issue": "exponential_\n\n", "before_files": [{"content": "# global\nimport ivy\nfrom ivy.func_wrapper import with_supported_dtypes\nfrom ivy.func_wrapper import with_supported_device_and_dtypes, with_unsupported_dtypes\nfrom ivy.functional.frontends.paddle.func_wrapper import (\n    to_ivy_arrays_and_back,\n)\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef normal(mean=0.0, std=1.0, shape=None, name=None):\n    return ivy.random_normal(mean=mean, std=std, shape=shape)\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef poisson(x, name=None):\n    return ivy.poisson(x, shape=None, device=None, dtype=None, seed=None, out=None)\n\n\n@with_supported_device_and_dtypes(\n    {\n        \"2.5.1 and above\": {\n            \"cpu\": (\n                \"bfloat16\",\n                \"float32\",\n                \"float64\",\n            ),\n            \"gpu\": (\n                \"bfloat16\",\n                \"float16\",\n                \"float32\",\n                \"float64\",\n            ),\n        },\n        \"2.4.2 and below\": {\n            \"cpu\": (\n                \"float32\",\n                \"float64\",\n            ),\n            \"gpu\": (\n                \"float16\",\n                \"float32\",\n                \"float64\",\n            ),\n        },\n    },\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef rand(shape, dtype=None, name=None):\n    return ivy.random_uniform(low=0.0, high=1.0, shape=shape, dtype=dtype, seed=None)\n\n\n@to_ivy_arrays_and_back\ndef randint(low=0, high=None, shape=[1], dtype=None, name=None):\n    return ivy.randint(low, high, shape=shape, dtype=dtype)\n\n\n@with_unsupported_dtypes(\n    {\"2.5.1 and below\": (\"int16\", \"float16\", \"bfloat16\", \"uint8\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef randint_like(x, low=0, high=None, dtype=None, name=None):\n    if high is None:\n        high = low\n        low = 0\n        if high <= 0:\n            raise ivy.exceptions.IvyError(\n                \"If high is None, low must be greater than 0, but received low = 0.\"\n            )\n    return ivy.randint(low, high, shape=x.shape, dtype=dtype, seed=None)\n\n\ndef randn(shape, dtype=None, name=None):\n    if dtype not in [\"float32\", \"float64\"]:\n        raise ivy.exceptions.IvyError(\n            \"Unsupported dtype for randn, only float32 and float64 are supported, \"\n        )\n    return ivy.random_normal(shape=shape, dtype=dtype, seed=None)\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef standard_normal(shape, dtype=None, name=None):\n    return ivy.random_normal(mean=0, std=1, shape=shape, dtype=dtype)\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None):\n    return ivy.random_uniform(low=min, high=max, shape=shape, dtype=dtype, seed=seed)\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef uniform_(x, min=-1.0, max=1.0, seed=0, name=None):\n    x = ivy.array(x)\n    return ivy.random_uniform(\n        low=min, high=max, shape=x.shape, dtype=x.dtype, seed=seed\n    )\n", "path": "ivy/functional/frontends/paddle/tensor/random.py"}], "after_files": [{"content": "# global\nimport ivy\nfrom ivy.func_wrapper import with_supported_dtypes\nfrom ivy.func_wrapper import with_supported_device_and_dtypes, with_unsupported_dtypes\nfrom ivy.functional.frontends.paddle.func_wrapper import (\n    to_ivy_arrays_and_back,\n)\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef exponential_(x, lam=1.0, name=None):\n    return ivy.multiply(lam, ivy.exp(ivy.multiply(-lam, 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 normal(mean=0.0, std=1.0, shape=None, name=None):\n    return ivy.random_normal(mean=mean, std=std, shape=shape)\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef poisson(x, name=None):\n    return ivy.poisson(x, shape=None, device=None, dtype=None, seed=None, out=None)\n\n\n@with_supported_device_and_dtypes(\n    {\n        \"2.5.1 and above\": {\n            \"cpu\": (\n                \"bfloat16\",\n                \"float32\",\n                \"float64\",\n            ),\n            \"gpu\": (\n                \"bfloat16\",\n                \"float16\",\n                \"float32\",\n                \"float64\",\n            ),\n        },\n        \"2.4.2 and below\": {\n            \"cpu\": (\n                \"float32\",\n                \"float64\",\n            ),\n            \"gpu\": (\n                \"float16\",\n                \"float32\",\n                \"float64\",\n            ),\n        },\n    },\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef rand(shape, dtype=None, name=None):\n    return ivy.random_uniform(low=0.0, high=1.0, shape=shape, dtype=dtype, seed=None)\n\n\n@to_ivy_arrays_and_back\ndef randint(low=0, high=None, shape=[1], dtype=None, name=None):\n    return ivy.randint(low, high, shape=shape, dtype=dtype)\n\n\n@with_unsupported_dtypes(\n    {\"2.5.1 and below\": (\"int16\", \"float16\", \"bfloat16\", \"uint8\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef randint_like(x, low=0, high=None, dtype=None, name=None):\n    if high is None:\n        high = low\n        low = 0\n        if high <= 0:\n            raise ivy.exceptions.IvyError(\n                \"If high is None, low must be greater than 0, but received low = 0.\"\n            )\n    return ivy.randint(low, high, shape=x.shape, dtype=dtype, seed=None)\n\n\ndef randn(shape, dtype=None, name=None):\n    if dtype not in [\"float32\", \"float64\"]:\n        raise ivy.exceptions.IvyError(\n            \"Unsupported dtype for randn, only float32 and float64 are supported, \"\n        )\n    return ivy.random_normal(shape=shape, dtype=dtype, seed=None)\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef standard_normal(shape, dtype=None, name=None):\n    return ivy.random_normal(mean=0, std=1, shape=shape, dtype=dtype)\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef uniform(shape, dtype=None, min=-1.0, max=1.0, seed=0, name=None):\n    return ivy.random_uniform(low=min, high=max, shape=shape, dtype=dtype, seed=seed)\n\n\n@with_supported_dtypes(\n    {\"2.5.1 and below\": (\"float32\", \"float64\")},\n    \"paddle\",\n)\n@to_ivy_arrays_and_back\ndef uniform_(x, min=-1.0, max=1.0, seed=0, name=None):\n    x = ivy.array(x)\n    return ivy.random_uniform(\n        low=min, high=max, shape=x.shape, dtype=x.dtype, seed=seed\n    )\n", "path": "ivy/functional/frontends/paddle/tensor/random.py"}]}