class_docstr: A structured representation of a policy output over a full (flat) environment step.
class_docstr_tok: ["A","structured","representation","of","a","policy","output","over","a","full","(","flat",")","environment","step","."]
docstr: Compute the action log probs for given actions.
  :param actions: The actions to use.
  :return: The computed action log probabilities.
docstr_tok: ["Compute","the","action","log","probs","for","given","actions",".",":","param","actions",":","The","actions","to","use",".",":","return",":","The","computed","action","log","probabilities","."]
code: def log_probs_for_actions(
      self, actions: List[TorchActionType]
  ) -> List[TorchActionType]:
      """Compute the action log probs for given actions.
      :param actions: The actions to use.
      :return: The computed action log probabilities.
      """
      assert len(self.prob_dist) == len(actions)
      return [
          pb.log_prob(ac) for pb, ac in zip(self.prob_dist, actions)
      ]
  
file_path: maze/maze/core/agent/torch_policy_output.py
imports: import dataclasses
  from typing import List, Dict, Optional
  import torch
  from maze.core.env.action_conversion import TorchActionType
  from maze.core.env.structured_env import ActorID
  from maze.distributions.dict import DictProbabilityDistribution
label_desc: Use image node_id 9 for calling the PolicyOutput obj's underlying member method code with example usage: obj.log_probs_for_actions(actions) and returns: unknown