code: def __init__(
      self,
      agent_parameters: AgentParameters,
      spaces: SpacesDefinition,
      network_name: str,
      head_idx: int = 0,
      loss_weight: float = 1.0,
      is_local: bool = True,
      activation_function: str = "tanh",
      dense_layer=Dense,
  ):
      super().__init__(
          agent_parameters,
          spaces,
          network_name,
          head_idx,
          loss_weight,
          is_local,
          activation_function,
          dense_layer=dense_layer,
      )
      self.name = "ppo_head"
      self.return_type = ActionProbabilities
  
      # used in regular PPO
      self.use_kl_regularization = (
          agent_parameters.algorithm.use_kl_regularization
      )
      if self.use_kl_regularization:
          # kl coefficient and its corresponding assignment operation and placeholder
          self.kl_coefficient = tf.Variable(
              agent_parameters.algorithm.initial_kl_coefficient,
              trainable=False,
              name="kl_coefficient",
          )
          self.kl_coefficient_ph = tf.placeholder(
              "float", name="kl_coefficient_ph"
          )
          self.assign_kl_coefficient = tf.assign(
              self.kl_coefficient, self.kl_coefficient_ph
          )
          self.kl_cutoff = (
              2 * agent_parameters.algorithm.target_kl_divergence
          )
          self.high_kl_penalty_coefficient = (
              agent_parameters.algorithm.high_kl_penalty_coefficient
          )
  
      self.clip_likelihood_ratio_using_epsilon = (
          agent_parameters.algorithm.clip_likelihood_ratio_using_epsilon
      )
      self.beta = agent_parameters.algorithm.beta_entropy
  
file_path: coach/rl_coach/architectures/tensorflow_components/heads/ppo_head.py
imports: import numpy
  import tensorflow
  from rl_coach.architectures.tensorflow_components.layers import Dense
  from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer
  from rl_coach.base_parameters import AgentParameters, DistributedTaskParameters
  from rl_coach.core_types import ActionProbabilities
  from rl_coach.spaces import BoxActionSpace, DiscreteActionSpace
  from rl_coach.spaces import SpacesDefinition
  from rl_coach.utils import eps
label_desc: Use image node_id 1 to create a new PPOHead object from inherited base classes: Head with example: obj = PPOHead(agent_parameters, spaces, network_name, head_idx, loss_weight, is_local, activation_function, dense_layer)