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 build(self, input_shape):
      param_shape = list(input_shape[1:])
      self.tau = self.add_weight(
          shape=param_shape,
          name="tau",
          initializer=self.tau_initializer,
          regularizer=self.tau_regularizer,
          constraint=self.tau_constraint,
          synchronization=tf.VariableSynchronization.AUTO,
          aggregation=tf.VariableAggregation.MEAN,
      )
      if self.affine:
          self.alpha = self.add_weight(
              shape=param_shape,
              name="alpha",
              initializer=self.alpha_initializer,
              regularizer=self.alpha_regularizer,
              constraint=self.alpha_constraint,
              synchronization=tf.VariableSynchronization.AUTO,
              aggregation=tf.VariableAggregation.MEAN,
          )
  
      axes = {i: input_shape[i] for i in range(1, len(input_shape))}
      self.input_spec = tf.keras.layers.InputSpec(
          ndim=len(input_shape), axes=axes
      )
      self.built = True
  
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 2 for calling the TLU obj's underlying member method code with example usage: obj.build(input_shape) without return types