class_docstr: ADULT encoder used in the Counterfactual with Reinforcement Learning experiments. The model consists of
  two fully connected layers with ReLU and tanh nonlinearities. The tanh nonlinearity clips the embedding in [-1, 1]
  as required in the DDPG algorithm (e.g., [act_low, act_high]). The layers' dimensions used in the paper are
  128 and 15, although those can vary as they were selected to generalize across many datasets.
docstr: Forward pass.
  
  Parameters
  ----------
  x
      Input tensor.
  **kwargs
      Other arguments.
  
  Returns
  -------
  Encoding representation having each component in the interval [-1, 1].
docstr_tok: ["Forward","pass",".","Parameters","--","--","--","--","--","x","Input","tensor",".","*","*","kwargs","Other","arguments",".","Returns","--","--","--","-","Encoding","representation","having","each","component","in","the","interval","[","-1",",","1","]","."]
code: def call(self, x: tf.Tensor, **kwargs) -> tf.Tensor:
      """
      Forward pass.
  
      Parameters
      ----------
      x
          Input tensor.
      **kwargs
          Other arguments.
  
      Returns
      -------
      Encoding representation having each component in the interval [-1, 1].
      """
      x = tf.nn.relu(self.fc1(x))
      x = tf.nn.tanh(self.fc2(x))
      return x
  
imports: import tensorflow
  import tensorflow.keras
  from typing import List
label_desc: Use image node_id 2 for calling the ADULTEncoder obj's underlying member method code with example usage: obj.call(x) and returns: x