class_docstr: Stores multi-agent episode data.
  
  The central attribute of the class is the timestep mapping
  `global_t_to_local_t` that maps the global (environment)
  timestep to the local (agent) timesteps.
  
  The `MultiAgentEpisode` is based on the `SingleAgentEpisode`s
  for each agent, stored in `MultiAgentEpisode.agent_episodes`.
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code: def _generate_action_timestep_mappings(self):
      # Note, here we use the indices that are from the predecessor, i.e.
      # these timesteps do not occur in the successor. This will have
      # the consequences that `get_actions()` will not list any actions
      # that come from the buffers of the predecessor.
      return {
          agent_id: _IndexMapping([self.global_actions_t[agent_id][-1]])
          if agent_buffer["actions"].full()
          else _IndexMapping()
          for agent_id, agent_buffer in self.agent_buffers.items()
      }
  
imports: from collections import defaultdict
  from queue import Queue
  from typing import Any, Dict, List, Optional, Set, Union
  import uuid
  import numpy
  from ray.rllib.env.single_agent_episode import SingleAgentEpisode
  from ray.rllib.policy.sample_batch import MultiAgentBatch
  from ray.rllib.utils.typing import MultiAgentDict
label_desc: Use image node_id 19 for calling the MultiAgentEpisode obj's underlying member method code with example usage: obj._generate_action_timestep_mappings() and returns: unknown