class_docstr: S-shaped Rectified Linear Unit.
  It follows:
  f(x) = t^r + a^r(x - t^r) for x >= t^r
  f(x) = x for t^r > x > t^l,
  f(x) = t^l + a^l(x - t^l) for x <= t^l
  
  When you use this layer as the first layer of a model, you need to provide the argument
  input_shape (a shape tuple, does not include the batch dimension).
  
  # Arguments
  t_left_init: String representation of the initialization method for the left part intercept.
               Default is 'zero'.
  a_left_init: String representation of the initialization method for the left part slope.
               Default is 'glorot_uniform'.
  t_right_init: String representation of the nitialization method for the right part intercept.
                Default is 'glorot_uniform'.
  a_right_init: String representation of the initialization method for the right part slope.
                Default is 'one'.
  shared_axes: Int tuple. The axes along which to share learnable parameters for the
               activation function. Default is None.
               For example, if the incoming feature maps are from a 2D convolution with output
               shape (batch, height, width, channels), and you wish to share parameters across
               space so that each filter only has one set of parameters, set 'shared_axes=(1,2)'.
  input_shape: A shape tuple, not including batch.
  name: String to set the name of the layer.
        If not specified, its name will by default to be a generated string.
  
  >>> srelu = SReLU(input_shape=(4, 5))
  creating: createZooKerasSReLU
code: def __init__(
      self,
      t_left_init="zero",
      a_left_init="glorot_uniform",
      t_right_init="glorot_uniform",
      a_right_init="one",
      shared_axes=None,
      input_shape=None,
      **kwargs,
  ):
      super(SReLU, self).__init__(
          None,
          t_left_init,
          a_left_init,
          t_right_init,
          a_right_init,
          shared_axes,
          list(input_shape) if input_shape else None,
          **kwargs,
      )
  
file_path: analytics-zoo/pyzoo/zoo/pipeline/api/keras/layers/advanced_activations.py
imports: import sys
  from ..engine.topology import ZooKerasLayer
label_desc: Use image node_id 1 to create a new SReLU object from inherited base classes: ZooKerasLayer with example: obj = SReLU(t_left_init, a_left_init, t_right_init, a_right_init, shared_axes, input_shape)