docstr: returns a list of tensorflow batchnorm, activation and dropout layers
  :param input_layer: previous layer
  :param name: layer name
  :return: batchnorm, activation and dropout layers
docstr_tok: ["returns","a","list","of","tensorflow","batchnorm",",","activation","and","dropout","layers",":","param","input_layer",":","previous","layer",":","param","name",":","layer","name",":","return",":","batchnorm",",","activation","and","dropout","layers"]
code: def __call__(self, input_layer, name: str = None, is_training=None):
      """
      returns a list of tensorflow batchnorm, activation and dropout layers
      :param input_layer: previous layer
      :param name: layer name
      :return: batchnorm, activation and dropout layers
      """
      return batchnorm_activation_dropout(
          input_layer,
          batchnorm=self.batchnorm,
          activation_function=self.activation_function,
          dropout_rate=self.dropout_rate,
          is_training=is_training,
          name=name,
      )
  
imports: import math
  from types import FunctionType
  import tensorflow
  from rl_coach.architectures import layers
  from rl_coach.architectures.tensorflow_components import utils
label_desc: Use image node_id 2 for calling the BatchnormActivationDropout obj's underlying member method code with example usage: obj.__call__(input_layer, name, is_training) and returns: batchnorm_activation_dropout