class_docstr: Critic network. The network follows the standard actor-critic architecture used in Deep Reinforcement Learning.
  The model is used in Counterfactual with Reinforcement Learning (CFRL) for both data modalities (images and
  tabular). The hidden dimension used for the all experiments is 256, which is a common choice in most benchmarks.
docstr: Forward pass.
  
  Parameters
  ----------
  x
      Input tensor.
  
  Returns
  -------
  Critic value.
code: def call(self, x: tf.Tensor, **kwargs) -> tf.Tensor:
      """
      Forward pass.
  
      Parameters
      ----------
      x
          Input tensor.
  
      Returns
      -------
      Critic value.
      """
      x = tf.nn.relu(self.ln1(self.fc1(x)))
      x = tf.nn.relu(self.ln2(self.fc2(x)))
      x = self.fc3(x)
      return x
  
file_path: alibi/alibi/models/tensorflow/actor_critic.py
imports: import tensorflow
  import tensorflow.keras
label_desc: Use image node_id 2 for calling the Critic obj's underlying member method code with example usage: obj.call(x) and returns: x