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`.
class_docstr_tok: ["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","`","."]
code: def _generate_global_actions_t(self, actions):
      global_actions_t = {
          agent_id: _IndexMapping() for agent_id in self._agent_ids
      }
      if actions:
          for t, action in enumerate(actions):
              for agent_id in action:
                  # Note, actions start at timestep 1, i.e. no initial actions.
                  # TODO (simon): Check, if need here also the `ts_carriage_return`.
                  global_actions_t[agent_id].append(
                      t + self.ts_carriage_return + 1
                  )
  
      return global_actions_t
  
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 22 for calling the MultiAgentEpisode obj's underlying member method code with example usage: obj._generate_global_actions_t(actions) and returns: global_actions_t