prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  softshrink
  
  
  --- 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 from ivy.functional.frontends.paddle.tensor.math import (
  7     log_softmax as paddle_log_softmax,
  8 )
  9 
  10 
  11 @with_supported_dtypes({"2.4.2 and below": ("float32", "float64")}, "paddle")
  12 @to_ivy_arrays_and_back
  13 def selu(
  14     x,
  15     /,
  16     *,
  17     alpha=1.6732632423543772848170429916717,
  18     scale=1.0507009873554804934193349852946,
  19     name=None,
  20 ):
  21     if scale <= 1.0:
  22         raise ValueError(f"The scale must be greater than 1.0. Received: {scale}.")
  23 
  24     if alpha < 0:
  25         raise ValueError(f"The alpha must be no less than zero. Received: {alpha}.")
  26 
  27     ret = ivy.where(x > 0, x, alpha * ivy.expm1(x))
  28     arr = scale * ret
  29     return ivy.astype(arr, x.dtype)
  30 
  31 
  32 tanh = paddle_tanh
  33 log_softmax = paddle_log_softmax
  34 
  35 
  36 @with_supported_dtypes({"2.4.2 and below": ("float32", "float64")}, "paddle")
  37 @to_ivy_arrays_and_back
  38 def hardshrink(x, threshold=0.5, name=None):
  39     mask = ivy.logical_or(ivy.greater(x, threshold), ivy.less(x, -threshold))
  40     return ivy.where(mask, x, 0.0)
  41 
  42 
  43 @with_supported_dtypes({"2.4.2 and below": ("float32", "float64")}, "paddle")
  44 @to_ivy_arrays_and_back
  45 def hardtanh(
  46     x,
  47     /,
  48     *,
  49     min=-1.0,
  50     max=1.0,
  51     name=None,
  52 ):
  53     less = ivy.where(ivy.less(x, min), min, x)
  54     ret = ivy.where(ivy.greater(x, max), max, less).astype(x.dtype)
  55     return ret
  56 
  57 
  58 @with_supported_dtypes({"2.4.2 and below": ("float32", "float64")}, "paddle")
  59 @to_ivy_arrays_and_back
  60 def gelu(x, approximate=False, name=None):
  61     return ivy.gelu(x, approximate=approximate)
  62 
  63 
  64 @with_supported_dtypes({"2.4.2 and below": ("float32", "float64")}, "paddle")
  65 @to_ivy_arrays_and_back
  66 def hardsigmoid(x, slope=0.1666667, offset=0.5, name=None):
  67     ret = ivy.minimum(ivy.maximum(ivy.add(ivy.multiply(x, slope), offset), 0), 1)
  68     return ret
  69 
  70 
  71 @with_supported_dtypes({"2.4.2 and below": ("float32", "float64")}, "paddle")
  72 @to_ivy_arrays_and_back
  73 def relu6(x, name=None):
  74     return ivy.relu6(x)
  75
  ```
  --- 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()
  
  ```
  
golden_diff: 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
  @@ -72,3 +72,18 @@
   @to_ivy_arrays_and_back
   def relu6(x, name=None):
       return ivy.relu6(x)
  +
  +
  +@with_supported_dtypes({"2.4.2 and below": ("float32", "float64")}, "paddle")
  +@to_ivy_arrays_and_back
  +def softshrink(
  +    x,
  +    /,
  +    *,
  +    threshold=0.5,
  +    name=None,
  +):
  +    low = ivy.where(ivy.less(x, -threshold), ivy.add(x, threshold), 0)
  +    up = ivy.where(ivy.greater(x, threshold), ivy.subtract(x, threshold), 0)
  +    add = ivy.add(low, up)
  +    return ivy.astype(add, x.dtype)
  
verification_info: {"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@@ -72,3 +72,18 @@\n @to_ivy_arrays_and_back\n def relu6(x, name=None):\n     return ivy.relu6(x)\n+\n+\n+@with_supported_dtypes({\"2.4.2 and below\": (\"float32\", \"float64\")}, \"paddle\")\n+@to_ivy_arrays_and_back\n+def 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", "issue": "softshrink\n\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\nfrom ivy.functional.frontends.paddle.tensor.math import (\n    log_softmax as paddle_log_softmax,\n)\n\n\n@with_supported_dtypes({\"2.4.2 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\nlog_softmax = paddle_log_softmax\n\n\n@with_supported_dtypes({\"2.4.2 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({\"2.4.2 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({\"2.4.2 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({\"2.4.2 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({\"2.4.2 and below\": (\"float32\", \"float64\")}, \"paddle\")\n@to_ivy_arrays_and_back\ndef relu6(x, name=None):\n    return ivy.relu6(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\nfrom ivy.functional.frontends.paddle.tensor.math import (\n    log_softmax as paddle_log_softmax,\n)\n\n\n@with_supported_dtypes({\"2.4.2 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\nlog_softmax = paddle_log_softmax\n\n\n@with_supported_dtypes({\"2.4.2 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({\"2.4.2 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({\"2.4.2 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({\"2.4.2 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({\"2.4.2 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({\"2.4.2 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", "path": "ivy/functional/frontends/paddle/nn/functional/activation.py"}]}