code: def selu(x):
      """Scaled Exponential Linear Unit (SELU).
  
      The Scaled Exponential Linear Unit (SELU) activation function is defined as:
  
      - `scale * x` if `x > 0`
      - `scale * alpha * (exp(x) - 1)` if `x < 0`
  
      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 `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 `keras.initializers.LecunNormal` initializer)
      and the number of input units is "large enough"
      (see reference paper for more information).
  
      Args:
          x: Input tensor.
  
      Notes:
  
      - To be used together with the
          `keras.initializers.LecunNormal` initializer.
      - To be used together with the dropout variant
          `keras.layers.AlphaDropout` (rather than regular dropout).
  
      Reference:
  
      - [Klambauer et al., 2017](https://arxiv.org/abs/1706.02515)
      """
      return ops.selu(x)
  
imports: from keras import backend
  from keras import ops
  from keras.api_export import keras_export
label_desc: Use image node_id 6 for calling a global function with example usage: selu(x) and returns: ops