class_docstr: Actor 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: Constructor.
  
  Parameters
  ----------
  hidden_dim
      Hidden dimension
  output_dim
      Output dimension
code: def __init__(self, hidden_dim: int, output_dim: int, **kwargs):
      """
      Constructor.
  
      Parameters
      ----------
      hidden_dim
          Hidden dimension
      output_dim
          Output dimension
      """
      super().__init__(**kwargs)
      self.fc1 = keras.layers.Dense(hidden_dim)
      self.ln1 = keras.layers.LayerNormalization()
      self.fc2 = keras.layers.Dense(hidden_dim)
      self.ln2 = keras.layers.LayerNormalization()
      self.fc3 = keras.layers.Dense(output_dim)
  
file_path: alibi/alibi/models/tensorflow/actor_critic.py
imports: import tensorflow
  import tensorflow.keras
label_desc: Use image node_id 1 to create a new Actor object from inherited base classes: keras with example: obj = Actor(hidden_dim, output_dim)