code: def selu(x):
      """Scaled Exponential Linear Unit (SELU).
  
      The Scaled Exponential Linear Unit (SELU) activation function is defined as:
  
      - `if x > 0: return scale * x`
      - `if x < 0: return scale * alpha * (exp(x) - 1)`
  
      where `alpha` and `scale` are pre-defined constants
      (`alpha=1.67326324` and `scale=1.05070098`).
  
      Basically, the SELU activation function multiplies `scale` (> 1) with the
      output of the `tf.keras.activations.elu` function to ensure a slope larger
      than one for positive inputs.
  
      The values of `alpha` and `scale` are
      chosen so that the mean and variance of the inputs are preserved
      between two consecutive layers as long as the weights are initialized
      correctly (see `tf.keras.initializers.LecunNormal` initializer)
      and the number of input units is "large enough"
      (see reference paper for more information).
  
      Example Usage:
  
      >>> num_classes = 10  # 10-class problem
      >>> model = tf.keras.Sequential()
      >>> model.add(tf.keras.layers.Dense(64, kernel_initializer='lecun_normal',
      ...                                 activation='selu'))
      >>> model.add(tf.keras.layers.Dense(32, kernel_initializer='lecun_normal',
      ...                                 activation='selu'))
      >>> model.add(tf.keras.layers.Dense(16, kernel_initializer='lecun_normal',
      ...                                 activation='selu'))
      >>> model.add(tf.keras.layers.Dense(num_classes, activation='softmax'))
  
      Args:
          x: A tensor or variable to compute the activation function for.
  
      Returns:
          The scaled exponential unit activation: `scale * elu(x, alpha)`.
  
      Notes:
          - To be used together with the
            `tf.keras.initializers.LecunNormal` initializer.
          - To be used together with the dropout variant
            `tf.keras.layers.AlphaDropout` (not regular dropout).
  
      References:
          - [Klambauer et al., 2017](https://arxiv.org/abs/1706.02515)
      """
      return nn.selu(x)
  
imports: from tensorflow.python.keras import backend
  from tensorflow.python.keras.layers import advanced_activations
  from tensorflow.python.keras.utils.generic_utils import deserialize_keras_object
  from tensorflow.python.keras.utils.generic_utils import serialize_keras_object
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import nn
  from tensorflow.python.util import dispatch
label_desc: Use image node_id 3 for calling a global function with example usage: selu(x) and returns: nn