code: def elu(x, alpha=1.0):
      """Exponential Linear Unit.
  
      The exponential linear unit (ELU) with `alpha > 0` is:
      `x` if `x > 0` and
      `alpha * (exp(x) - 1)` if `x < 0`
      The ELU hyperparameter `alpha` controls the value to which an
      ELU saturates for negative net inputs. ELUs diminish the
      vanishing gradient effect.
  
      ELUs have negative values which pushes the mean of the activations
      closer to zero.
      Mean activations that are closer to zero enable faster learning as they
      bring the gradient closer to the natural gradient.
      ELUs saturate to a negative value when the argument gets smaller.
      Saturation means a small derivative which decreases the variation
      and the information that is propagated to the next layer.
  
      Example Usage:
  
      >>> import tensorflow as tf
      >>> model = tf.keras.Sequential()
      >>> model.add(tf.keras.layers.Conv2D(32, (3, 3), activation='elu',
      ...          input_shape=(28, 28, 1)))
      >>> model.add(tf.keras.layers.MaxPooling2D((2, 2)))
      >>> model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='elu'))
      >>> model.add(tf.keras.layers.MaxPooling2D((2, 2)))
      >>> model.add(tf.keras.layers.Conv2D(64, (3, 3), activation='elu'))
  
      <tensorflow.python.keras.engine.sequential.Sequential object ...>
  
      Args:
          x: Input tensor.
          alpha: A scalar, slope of negative section. `alpha` controls the value to
            which an ELU saturates for negative net inputs.
  
      Returns:
          The exponential linear unit (ELU) activation function: `x` if `x > 0` and
          `alpha * (exp(x) - 1)` if `x < 0`.
  
  
      Reference:
          [Fast and Accurate Deep Network Learning by Exponential Linear Units
          (ELUs) (Clevert et al, 2016)](https://arxiv.org/abs/1511.07289)
      """
      return backend.elu(x, alpha)
  
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 2 for calling a global function with example usage: elu(x, alpha) and returns: backend