class_docstr: This is a Ray Train-like execution flow.
  
  - We want to run 4 actors in total ("trials")
  - Each actor runs two init functions
  - We train all actors in parallel for 10 iterations
  - Errors can come up on actor construction, in the init functions,
    or during training
  - When an actor fails, restart that actor
  - When a task fails, stop actor, and restart
class_docstr_tok: ["This","is","a","Ray","Train-like","execution","flow",".","-","We","want","to","run","4","actors","in","total","(","``","trials","''",")","-","Each","actor","runs","two","init","functions","-","We","train","all","actors","in","parallel","for","10","iterations","-","Errors","can","come","up","on","actor","construction",",","in","the","init","functions",",","or","during","training","-","When","an","actor","fails",",","restart","that","actor","-","When","a","task","fails",",","stop","actor",",","and","restart"]
code: def _replace_actor(self, tracked_actor: TrackedActor):
      actor_index = self._tracked_actors.index(tracked_actor)
  
      replacement_actor = self._actor_manager.add_actor(
          cls=Actor,
          kwargs={"id": actor_index},
          resource_request=ResourceRequest([{"CPU": 1}]),
          on_start=self.actor_started,
          on_stop=self.actor_stopped,
          on_error=self.actor_error,
      )
  
      self._tracked_actors[actor_index] = replacement_actor
  
imports: import random
  from typing import Any, List, Optional
  import pytest
  import ray
  from ray.air import ResourceRequest
  from ray.air.execution import FixedResourceManager, PlacementGroupResourceManager
  from ray.air.execution._internal import Barrier
  from ray.air.execution._internal.actor_manager import RayActorManager
  from ray.air.execution._internal.tracked_actor import TrackedActor
  from ray.exceptions import RayActorError
label_desc: Use image node_id 6 for calling the TrainFlow obj's underlying member method code with example usage: obj._replace_actor(tracked_actor) without return types