code: def target_entropy_from_env_spec(
      spec: specs.EnvironmentSpec,
      target_entropy_per_dimension: Optional[float] = None,
  ) -> float:
      """A heuristic to determine a target entropy.
  
      If target_entropy_per_dimension is not specified, the target entropy is
      computed as "-num_actions", otherwise it is
      "target_entropy_per_dimension * num_actions".
  
      Args:
        spec: environment spec
        target_entropy_per_dimension: None or target entropy per action dimension
  
      Returns:
        target entropy
      """
  
      def get_num_actions(action_spec: Any) -> float:
          """Returns a number of actions in the spec."""
          if isinstance(action_spec, specs.BoundedArray):
              return onp.prod(action_spec.shape, dtype=int)
          elif isinstance(action_spec, tuple):
              return sum(
                  get_num_actions(subspace) for subspace in action_spec
              )
          else:
              raise ValueError("Unknown action space type.")
  
      num_actions = get_num_actions(spec.actions)
      if target_entropy_per_dimension is None:
          if not isinstance(
              spec.actions, specs.BoundedArray
          ) or isinstance(spec.actions, specs.DiscreteArray):
              raise ValueError(
                  "Only accept BoundedArrays for automatic "
                  f"target_entropy, got: {spec.actions}"
              )
          if not onp.all(spec.actions.minimum == -1.0):
              raise ValueError(
                  f"Minimum expected to be -1, got: {spec.actions.minimum}"
              )
          if not onp.all(spec.actions.maximum == 1.0):
              raise ValueError(
                  f"Maximum expected to be 1, got: {spec.actions.maximum}"
              )
  
          return -num_actions
      else:
          return target_entropy_per_dimension * num_actions
  
file_path: acme/acme/agents/jax/sac/config.py
imports: import dataclasses
  from typing import Any, Optional
  from acme import specs
  from acme.adders import reverb
  from acme.agents.jax import normalization
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: target_entropy_from_env_spec(spec, target_entropy_per_dimension) and returns: unknown, unknown, onp, sum