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 __init__(
      self,
      affine: bool = False,
      tau_initializer: types.Initializer = "zeros",
      tau_regularizer: types.Regularizer = None,
      tau_constraint: types.Constraint = None,
      alpha_initializer: types.Initializer = "zeros",
      alpha_regularizer: types.Regularizer = None,
      alpha_constraint: types.Constraint = None,
      **kwargs,
  ):
      super().__init__(**kwargs)
      self.supports_masking = True
      self.affine = affine
      self.tau_initializer = tf.keras.initializers.get(tau_initializer)
      self.tau_regularizer = tf.keras.regularizers.get(tau_regularizer)
      self.tau_constraint = tf.keras.constraints.get(tau_constraint)
      if self.affine:
          self.alpha_initializer = tf.keras.initializers.get(
              alpha_initializer
          )
          self.alpha_regularizer = tf.keras.regularizers.get(
              alpha_regularizer
          )
          self.alpha_constraint = tf.keras.constraints.get(
              alpha_constraint
          )
  
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 1 to create a new TLU object from inherited base classes: tf.keras.layers with example: obj = TLU(affine, tau_initializer, tau_regularizer, tau_constraint, alpha_initializer, alpha_regularizer, alpha_constraint)