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

--- 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/nn/functional/activation.py`
Content:
```
1 # local
2 import ivy
3 from ivy.func_wrapper import with_supported_dtypes
4 from ivy.functional.frontends.paddle.func_wrapper import to_ivy_arrays_and_back
5 from ivy.functional.frontends.paddle.tensor.math import tanh as paddle_tanh
6 
7 
8 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
9 @to_ivy_arrays_and_back
10 def selu(
11     x,
12     /,
13     *,
14     alpha=1.6732632423543772848170429916717,
15     scale=1.0507009873554804934193349852946,
16     name=None,
17 ):
18     if scale <= 1.0:
19         raise ValueError(f"The scale must be greater than 1.0. Received: {scale}.")
20 
21     if alpha < 0:
22         raise ValueError(f"The alpha must be no less than zero. Received: {alpha}.")
23 
24     ret = ivy.where(x > 0, x, alpha * ivy.expm1(x))
25     arr = scale * ret
26     return ivy.astype(arr, x.dtype)
27 
28 
29 tanh = paddle_tanh
30 
31 
32 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
33 @to_ivy_arrays_and_back
34 def hardshrink(x, threshold=0.5, name=None):
35     mask = ivy.logical_or(ivy.greater(x, threshold), ivy.less(x, -threshold))
36     return ivy.where(mask, x, 0.0)
37 
38 
39 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
40 @to_ivy_arrays_and_back
41 def hardswish(x, name=None):
42     relu6_val = ivy.relu6(ivy.add(x, 3))
43     ret = ivy.multiply(x, ivy.divide(relu6_val, 6))
44     return ret
45 
46 
47 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
48 @to_ivy_arrays_and_back
49 def hardtanh(
50     x,
51     /,
52     *,
53     min=-1.0,
54     max=1.0,
55     name=None,
56 ):
57     less = ivy.where(ivy.less(x, min), min, x)
58     ret = ivy.where(ivy.greater(x, max), max, less).astype(x.dtype)
59     return ret
60 
61 
62 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
63 @to_ivy_arrays_and_back
64 def gelu(x, approximate=False, name=None):
65     return ivy.gelu(x, approximate=approximate)
66 
67 
68 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
69 @to_ivy_arrays_and_back
70 def hardsigmoid(x, slope=0.1666667, offset=0.5, name=None):
71     ret = ivy.minimum(ivy.maximum(ivy.add(ivy.multiply(x, slope), offset), 0), 1)
72     return ret
73 
74 
75 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
76 @to_ivy_arrays_and_back
77 def relu6(x, name=None):
78     return ivy.relu6(x)
79 
80 
81 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
82 @to_ivy_arrays_and_back
83 def softshrink(
84     x,
85     /,
86     *,
87     threshold=0.5,
88     name=None,
89 ):
90     low = ivy.where(ivy.less(x, -threshold), ivy.add(x, threshold), 0)
91     up = ivy.where(ivy.greater(x, threshold), ivy.subtract(x, threshold), 0)
92     add = ivy.add(low, up)
93     return ivy.astype(add, x.dtype)
94 
95 
96 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
97 @to_ivy_arrays_and_back
98 def softsign(
99     x,
100     /,
101     *,
102     name=None,
103 ):
104     return ivy.divide(x, ivy.add(1, ivy.abs(x)))
105 
106 
107 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
108 @to_ivy_arrays_and_back
109 def log_softmax(x, axis=-1, dtype=None, name=None):
110     x = ivy.astype(x, dtype) if dtype else x
111     ret = ivy.log_softmax(x, axis=axis)
112     ret = ivy.astype(ret, dtype) if dtype else ret
113     return ret
114 
115 
116 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
117 @to_ivy_arrays_and_back
118 def prelu(x, weight, data_format="NCHW", name=None):
119     return ivy.add(ivy.maximum(0, x), ivy.multiply(weight, ivy.minimum(0, x)))
120 
121 
122 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
123 @to_ivy_arrays_and_back
124 def celu(
125     x,
126     /,
127     *,
128     alpha=1.0,
129     name=None,
130 ):
131     prod = alpha * (ivy.exp(x / alpha) - 1)
132     ret = ivy.maximum(0, x) + ivy.minimum(0, prod)
133     return ret
134 
135 
136 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
137 @to_ivy_arrays_and_back
138 def rrelu(
139     x,
140     /,
141     *,
142     lower=0.125,
143     upper=0.3333333333333333,
144     training=False,
145     name=None,
146 ):
147     if lower < 0 or lower > 1:
148         raise ValueError(
149             "The lower value must be no less than zero or greater than one. Received:"
150             f" {lower}."
151         )
152 
153     if upper < lower:
154         raise ValueError(
155             "The upper value must be greater than lower value. Received: lower"
156             f" {lower}, upper {upper}."
157         )
158 
159     if upper > 1:
160         raise ValueError(
161             f"The upper value must be no greater than one. Received: {upper}."
162         )
163 
164     is_test = not training
165     if is_test:
166         add = lower + upper
167         ret = add * x * 0.5
168         out = ivy.where(x >= 0, x, ret)
169         return out.astype(x.dtype)
170     # else:
171     # ToDo implement a correctly after fixing ivy.random_uniform
172     # a = ivy.random_normal(low=lower, high=upper)
173     # ret = ivy.where(x >= 0, x, ivy.multiply(a, x))
174     # return ret.astype(x.dtype)
175 
176 
177 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
178 @to_ivy_arrays_and_back
179 def tanhshrink(
180     x,
181     /,
182     *,
183     name=None,
184 ):
185     return ivy.subtract(x, ivy.tanh(x))
186 
187 
188 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
189 @to_ivy_arrays_and_back
190 def relu_(x, name=None):
191     ret = ivy.relu(x)
192     ivy.inplace_update(x, ret)
193     return x
194 
195 
196 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
197 @to_ivy_arrays_and_back
198 def elu(
199     x,
200     /,
201     *,
202     alpha=1.0,
203     name=None,
204 ):
205     return ivy.elu(x, alpha=alpha)
206 
207 
208 @with_supported_dtypes({"2.5.0 and below": ("float32", "float64")}, "paddle")
209 @to_ivy_arrays_and_back
210 def mish(x, name=None):
211     return ivy.mish(x)
212
```
--- 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/nn/functional/activation.py b/ivy/functional/frontends/paddle/nn/functional/activation.py
--- a/ivy/functional/frontends/paddle/nn/functional/activation.py
+++ b/ivy/functional/frontends/paddle/nn/functional/activation.py
@@ -209,3 +209,8 @@
 @to_ivy_arrays_and_back
 def mish(x, name=None):
     return ivy.mish(x)
+
+@with_supported_dtypes({"2.4.2 and below": ("float32", "float64")}, "paddle")
+@to_ivy_arrays_and_back
+def leaky_relu(x, negative_slope=0.01, name=None):
+    return ivy.leaky_relu(x)

{"golden_diff": "diff --git a/ivy/functional/frontends/paddle/nn/functional/activation.py b/ivy/functional/frontends/paddle/nn/functional/activation.py\n--- a/ivy/functional/frontends/paddle/nn/functional/activation.py\n+++ b/ivy/functional/frontends/paddle/nn/functional/activation.py\n@@ -209,3 +209,8 @@\n @to_ivy_arrays_and_back\n def mish(x, name=None):\n     return ivy.mish(x)\n+\n+@with_supported_dtypes({\"2.4.2 and below\": (\"float32\", \"float64\")}, \"paddle\")\n+@to_ivy_arrays_and_back\n+def leaky_relu(x, negative_slope=0.01, name=None):\n+    return ivy.leaky_relu(x)\n", "issue": "leaky_relu\nPaddle Frontend\n", "before_files": [{"content": "# local\nimport ivy\nfrom ivy.func_wrapper import with_supported_dtypes\nfrom ivy.functional.frontends.paddle.func_wrapper import to_ivy_arrays_and_back\nfrom ivy.functional.frontends.paddle.tensor.math import tanh as paddle_tanh\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef selu(\n    x,\n    /,\n    *,\n    alpha=1.6732632423543772848170429916717,\n    scale=1.0507009873554804934193349852946,\n    name=None,\n):\n    if scale <= 1.0:\n        raise ValueError(f\"The scale must be greater than 1.0. Received: {scale}.\")\n\n    if alpha < 0:\n        raise ValueError(f\"The alpha must be no less than zero. Received: {alpha}.\")\n\n    ret = ivy.where(x > 0, x, alpha * ivy.expm1(x))\n    arr = scale * ret\n    return ivy.astype(arr, x.dtype)\n\n\ntanh = paddle_tanh\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef hardshrink(x, threshold=0.5, name=None):\n    mask = ivy.logical_or(ivy.greater(x, threshold), ivy.less(x, -threshold))\n    return ivy.where(mask, x, 0.0)\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef hardswish(x, name=None):\n    relu6_val = ivy.relu6(ivy.add(x, 3))\n    ret = ivy.multiply(x, ivy.divide(relu6_val, 6))\n    return ret\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef hardtanh(\n    x,\n    /,\n    *,\n    min=-1.0,\n    max=1.0,\n    name=None,\n):\n    less = ivy.where(ivy.less(x, min), min, x)\n    ret = ivy.where(ivy.greater(x, max), max, less).astype(x.dtype)\n    return ret\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef gelu(x, approximate=False, name=None):\n    return ivy.gelu(x, approximate=approximate)\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef hardsigmoid(x, slope=0.1666667, offset=0.5, name=None):\n    ret = ivy.minimum(ivy.maximum(ivy.add(ivy.multiply(x, slope), offset), 0), 1)\n    return ret\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef relu6(x, name=None):\n    return ivy.relu6(x)\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef softshrink(\n    x,\n    /,\n    *,\n    threshold=0.5,\n    name=None,\n):\n    low = ivy.where(ivy.less(x, -threshold), ivy.add(x, threshold), 0)\n    up = ivy.where(ivy.greater(x, threshold), ivy.subtract(x, threshold), 0)\n    add = ivy.add(low, up)\n    return ivy.astype(add, x.dtype)\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef softsign(\n    x,\n    /,\n    *,\n    name=None,\n):\n    return ivy.divide(x, ivy.add(1, ivy.abs(x)))\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef log_softmax(x, axis=-1, dtype=None, name=None):\n    x = ivy.astype(x, dtype) if dtype else x\n    ret = ivy.log_softmax(x, axis=axis)\n    ret = ivy.astype(ret, dtype) if dtype else ret\n    return ret\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef prelu(x, weight, data_format=\"NCHW\", name=None):\n    return ivy.add(ivy.maximum(0, x), ivy.multiply(weight, ivy.minimum(0, x)))\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef celu(\n    x,\n    /,\n    *,\n    alpha=1.0,\n    name=None,\n):\n    prod = alpha * (ivy.exp(x / alpha) - 1)\n    ret = ivy.maximum(0, x) + ivy.minimum(0, prod)\n    return ret\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef rrelu(\n    x,\n    /,\n    *,\n    lower=0.125,\n    upper=0.3333333333333333,\n    training=False,\n    name=None,\n):\n    if lower < 0 or lower > 1:\n        raise ValueError(\n            \"The lower value must be no less than zero or greater than one. Received:\"\n            f\" {lower}.\"\n        )\n\n    if upper < lower:\n        raise ValueError(\n            \"The upper value must be greater than lower value. Received: lower\"\n            f\" {lower}, upper {upper}.\"\n        )\n\n    if upper > 1:\n        raise ValueError(\n            f\"The upper value must be no greater than one. Received: {upper}.\"\n        )\n\n    is_test = not training\n    if is_test:\n        add = lower + upper\n        ret = add * x * 0.5\n        out = ivy.where(x >= 0, x, ret)\n        return out.astype(x.dtype)\n    # else:\n    # ToDo implement a correctly after fixing ivy.random_uniform\n    # a = ivy.random_normal(low=lower, high=upper)\n    # ret = ivy.where(x >= 0, x, ivy.multiply(a, x))\n    # return ret.astype(x.dtype)\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef tanhshrink(\n    x,\n    /,\n    *,\n    name=None,\n):\n    return ivy.subtract(x, ivy.tanh(x))\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef relu_(x, name=None):\n    ret = ivy.relu(x)\n    ivy.inplace_update(x, ret)\n    return x\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef elu(\n    x,\n    /,\n    *,\n    alpha=1.0,\n    name=None,\n):\n    return ivy.elu(x, alpha=alpha)\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef mish(x, name=None):\n    return ivy.mish(x)\n", "path": "ivy/functional/frontends/paddle/nn/functional/activation.py"}], "after_files": [{"content": "# local\nimport ivy\nfrom ivy.func_wrapper import with_supported_dtypes\nfrom ivy.functional.frontends.paddle.func_wrapper import to_ivy_arrays_and_back\nfrom ivy.functional.frontends.paddle.tensor.math import tanh as paddle_tanh\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef selu(\n    x,\n    /,\n    *,\n    alpha=1.6732632423543772848170429916717,\n    scale=1.0507009873554804934193349852946,\n    name=None,\n):\n    if scale <= 1.0:\n        raise ValueError(f\"The scale must be greater than 1.0. Received: {scale}.\")\n\n    if alpha < 0:\n        raise ValueError(f\"The alpha must be no less than zero. Received: {alpha}.\")\n\n    ret = ivy.where(x > 0, x, alpha * ivy.expm1(x))\n    arr = scale * ret\n    return ivy.astype(arr, x.dtype)\n\n\ntanh = paddle_tanh\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef hardshrink(x, threshold=0.5, name=None):\n    mask = ivy.logical_or(ivy.greater(x, threshold), ivy.less(x, -threshold))\n    return ivy.where(mask, x, 0.0)\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef hardswish(x, name=None):\n    relu6_val = ivy.relu6(ivy.add(x, 3))\n    ret = ivy.multiply(x, ivy.divide(relu6_val, 6))\n    return ret\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef hardtanh(\n    x,\n    /,\n    *,\n    min=-1.0,\n    max=1.0,\n    name=None,\n):\n    less = ivy.where(ivy.less(x, min), min, x)\n    ret = ivy.where(ivy.greater(x, max), max, less).astype(x.dtype)\n    return ret\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef gelu(x, approximate=False, name=None):\n    return ivy.gelu(x, approximate=approximate)\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef hardsigmoid(x, slope=0.1666667, offset=0.5, name=None):\n    ret = ivy.minimum(ivy.maximum(ivy.add(ivy.multiply(x, slope), offset), 0), 1)\n    return ret\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef relu6(x, name=None):\n    return ivy.relu6(x)\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef softshrink(\n    x,\n    /,\n    *,\n    threshold=0.5,\n    name=None,\n):\n    low = ivy.where(ivy.less(x, -threshold), ivy.add(x, threshold), 0)\n    up = ivy.where(ivy.greater(x, threshold), ivy.subtract(x, threshold), 0)\n    add = ivy.add(low, up)\n    return ivy.astype(add, x.dtype)\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef softsign(\n    x,\n    /,\n    *,\n    name=None,\n):\n    return ivy.divide(x, ivy.add(1, ivy.abs(x)))\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef log_softmax(x, axis=-1, dtype=None, name=None):\n    x = ivy.astype(x, dtype) if dtype else x\n    ret = ivy.log_softmax(x, axis=axis)\n    ret = ivy.astype(ret, dtype) if dtype else ret\n    return ret\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef prelu(x, weight, data_format=\"NCHW\", name=None):\n    return ivy.add(ivy.maximum(0, x), ivy.multiply(weight, ivy.minimum(0, x)))\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef celu(\n    x,\n    /,\n    *,\n    alpha=1.0,\n    name=None,\n):\n    prod = alpha * (ivy.exp(x / alpha) - 1)\n    ret = ivy.maximum(0, x) + ivy.minimum(0, prod)\n    return ret\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef rrelu(\n    x,\n    /,\n    *,\n    lower=0.125,\n    upper=0.3333333333333333,\n    training=False,\n    name=None,\n):\n    if lower < 0 or lower > 1:\n        raise ValueError(\n            \"The lower value must be no less than zero or greater than one. Received:\"\n            f\" {lower}.\"\n        )\n\n    if upper < lower:\n        raise ValueError(\n            \"The upper value must be greater than lower value. Received: lower\"\n            f\" {lower}, upper {upper}.\"\n        )\n\n    if upper > 1:\n        raise ValueError(\n            f\"The upper value must be no greater than one. Received: {upper}.\"\n        )\n\n    is_test = not training\n    if is_test:\n        add = lower + upper\n        ret = add * x * 0.5\n        out = ivy.where(x >= 0, x, ret)\n        return out.astype(x.dtype)\n    # else:\n    # ToDo implement a correctly after fixing ivy.random_uniform\n    # a = ivy.random_normal(low=lower, high=upper)\n    # ret = ivy.where(x >= 0, x, ivy.multiply(a, x))\n    # return ret.astype(x.dtype)\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef tanhshrink(\n    x,\n    /,\n    *,\n    name=None,\n):\n    return ivy.subtract(x, ivy.tanh(x))\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef relu_(x, name=None):\n    ret = ivy.relu(x)\n    ivy.inplace_update(x, ret)\n    return x\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef elu(\n    x,\n    /,\n    *,\n    alpha=1.0,\n    name=None,\n):\n    return ivy.elu(x, alpha=alpha)\n\n\n@with_supported_dtypes({\"2.5.0 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef mish(x, name=None):\n    return ivy.mish(x)\n\n@with_supported_dtypes({\"2.4.2 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef leaky_relu(x, negative_slope=0.01, name=None):\n    return ivy.leaky_relu(x)\n", "path": "ivy/functional/frontends/paddle/nn/functional/activation.py"}]}