class_docstr: Thresholded Linear Unit.
  
  An activation function which is similar to ReLU
  but with a learned threshold that benefits models using FRN(Filter Response
  Normalization). Original paper: https://arxiv.org/pdf/1911.09737.
  
  Input shape:
      Arbitrary. Use the keyword argument `input_shape`
      (tuple of integers, does not include the samples axis)
      when using this layer as the first layer in a model.
  
  Output shape:
      Same shape as the input.
  
  Args:
      affine: `bool`. Whether to make it TLU-Affine or not
          which has the form $\max(x, \alpha*x + \tau)$`
code: def get_config(self):
      config = {
          "tau_initializer": tf.keras.initializers.serialize(
              self.tau_initializer
          ),
          "tau_regularizer": tf.keras.regularizers.serialize(
              self.tau_regularizer
          ),
          "tau_constraint": tf.keras.constraints.serialize(
              self.tau_constraint
          ),
          "affine": self.affine,
      }
  
      if self.affine:
          config["alpha_initializer"] = tf.keras.initializers.serialize(
              self.alpha_initializer
          )
          config["alpha_regularizer"] = tf.keras.regularizers.serialize(
              self.alpha_regularizer
          )
          config["alpha_constraint"] = tf.keras.constraints.serialize(
              self.alpha_constraint
          )
  
      base_config = super().get_config()
      return {**base_config, **config}
  
file_path: addons/tensorflow_addons/layers/tlu.py
imports: import tensorflow
  from typeguard import typechecked
  from tensorflow_addons.utils import types
label_desc: Use image node_id 4 for calling the TLU obj's underlying member method code with example usage: obj.get_config() and returns: dict