prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  selu
  
  
  </issue>
  <code>
  [start of ivy/functional/frontends/paddle/nn/functional/activation.py]
  1 # local
  2 from ivy.functional.frontends.paddle.tensor.math import tanh as paddle_tanh
  3 from ivy.functional.frontends.paddle.tensor.math import (
  4     log_softmax as paddle_log_softmax,
  5 )
  6 
  7 
  8 tanh = paddle_tanh
  9 log_softmax = paddle_log_softmax
  10
  [end of ivy/functional/frontends/paddle/nn/functional/activation.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
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
  @@ -1,9 +1,33 @@
   # local
  +import ivy
  +from ivy.func_wrapper import with_supported_dtypes
  +from ivy.functional.frontends.paddle.func_wrapper import to_ivy_arrays_and_back
   from ivy.functional.frontends.paddle.tensor.math import tanh as paddle_tanh
   from ivy.functional.frontends.paddle.tensor.math import (
       log_softmax as paddle_log_softmax,
   )
   
   
  +@with_supported_dtypes({"2.4.2 and below": ("float32", "float64")}, "paddle")
  +@to_ivy_arrays_and_back
  +def selu(
  +    x,
  +    /,
  +    *,
  +    alpha=1.6732632423543772848170429916717,
  +    scale=1.0507009873554804934193349852946,
  +    name=None,
  +):
  +    if scale <= 1.0:
  +        raise ValueError(f"The scale must be greater than 1.0. Received: {scale}.")
  +
  +    if alpha < 0:
  +        raise ValueError(f"The alpha must be no less than zero. Received: {alpha}.")
  +
  +    ret = ivy.where(x > 0, x, alpha * ivy.expm1(x))
  +    arr = scale * ret
  +    return ivy.astype(arr, x.dtype)
  +
  +
   tanh = paddle_tanh
   log_softmax = paddle_log_softmax
  
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@@ -1,9 +1,33 @@\n # local\n+import ivy\n+from ivy.func_wrapper import with_supported_dtypes\n+from ivy.functional.frontends.paddle.func_wrapper import to_ivy_arrays_and_back\n from ivy.functional.frontends.paddle.tensor.math import tanh as paddle_tanh\n from 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\n+def 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+\n tanh = paddle_tanh\n log_softmax = paddle_log_softmax\n", "issue": "selu\n\n", "before_files": [{"content": "# local\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\ntanh = paddle_tanh\nlog_softmax = paddle_log_softmax\n", "path": "ivy/functional/frontends/paddle/nn/functional/activation.py"}]}