code: def __str__(self):
      action_spaces = [self.spaces.action]
      if isinstance(self.spaces.action, CompoundActionSpace):
          action_spaces = self.spaces.action.sub_action_spaces
  
      result = []
      for action_space_idx, action_space in enumerate(action_spaces):
          action_head_mean_result = []
          if isinstance(action_space, DiscreteActionSpace):
              # create a discrete action network (softmax probabilities output)
              action_head_mean_result.append(
                  "Dense (num outputs = {})".format(
                      len(action_space.actions)
                  )
              )
              action_head_mean_result.append("Softmax")
          elif isinstance(action_space, BoxActionSpace):
              # create a continuous action network (bounded mean and stdev outputs)
              action_head_mean_result.append(
                  "Dense (num outputs = {})".format(action_space.shape)
              )
              if np.all(action_space.max_abs_range < np.inf):
                  # bounded actions
                  action_head_mean_result.append(
                      "Activation (type = {})".format(
                          self.activation_function.__name__
                      )
                  )
                  action_head_mean_result.append(
                      "Multiply (factor = {})".format(
                          action_space.max_abs_range
                      )
                  )
  
          action_head_stdev_result = []
          if isinstance(
              self.exploration_policy, ContinuousEntropyParameters
          ):
              action_head_stdev_result.append(
                  "Dense (num outputs = {})".format(action_space.shape)
              )
              action_head_stdev_result.append("Softplus")
  
          action_head_result = []
          if action_head_stdev_result:
              action_head_result.append("Mean Stream")
              action_head_result.append(
                  indent_string("\n".join(action_head_mean_result))
              )
              action_head_result.append("Stdev Stream")
              action_head_result.append(
                  indent_string("\n".join(action_head_stdev_result))
              )
          else:
              action_head_result.append(
                  "\n".join(action_head_mean_result)
              )
  
          if len(action_spaces) > 1:
              result.append("Action head {}".format(action_space_idx))
              result.append(
                  indent_string("\n".join(action_head_result))
              )
          else:
              result.append("\n".join(action_head_result))
  
      return "\n".join(result)
  
file_path: coach/rl_coach/architectures/tensorflow_components/heads/policy_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
  from rl_coach.core_types import ActionProbabilities
  from rl_coach.exploration_policies.continuous_entropy import ContinuousEntropyParameters
  from rl_coach.spaces import DiscreteActionSpace, BoxActionSpace, CompoundActionSpace
  from rl_coach.spaces import SpacesDefinition
  from rl_coach.utils import eps, indent_string
label_desc: Use image node_id 5 for calling the PolicyHead obj's underlying member method code with example usage: obj.__str__() and returns: str