class_docstr: Base class for distributed RL-style data collection from an environment.
  
  The EnvRunner API's core functionalities can be summarized as:
  - Gets configured via passing a AlgorithmConfig object to the constructor.
  Normally, subclasses of EnvRunner then construct their own environment (possibly
  vectorized) copies and RLModules/Policies and use the latter to step through the
  environment in order to collect training data.
  - Clients of EnvRunner can use the `sample()` method to collect data for training
  from the environment(s).
  - EnvRunner offers parallelism via creating n remote Ray Actors based on this class.
  Use `ray.remote([resources])(EnvRunner)` method to create the corresponding Ray
  remote class. Then instantiate n Actors using the Ray `[ctor].remote(...)` syntax.
  - EnvRunner clients can get information about the server/node on which the
  individual Actors are running.
docstr: Restores this EnvRunner's state from the given state dict.
  
  Args:
      state: The state dict to restore the state from.
  
  .. testcode::
      :skipif: True
  
      from ray.rllib.env.env_runner import EnvRunner
      env_runner = ...
      state = env_runner.get_state()
      new_runner = EnvRunner(...)
      new_runner.set_state(state)
docstr_tok: ["Restores","this","EnvRunner","'s","state","from","the","given","state","dict",".","Args",":","state",":","The","state","dict","to","restore","the","state","from",".","..","testcode",":",":",":","skipif",":","True","from","ray.rllib.env.env_runner","import","EnvRunner","env_runner","=","...","state","=","env_runner.get_state","(",")","new_runner","=","EnvRunner","(","...",")","new_runner.set_state","(","state",")"]
code: def set_state(self, state: Dict[str, Any]) -> None:
      """Restores this EnvRunner's state from the given state dict.
  
      Args:
          state: The state dict to restore the state from.
  
      .. testcode::
          :skipif: True
  
          from ray.rllib.env.env_runner import EnvRunner
          env_runner = ...
          state = env_runner.get_state()
          new_runner = EnvRunner(...)
          new_runner.set_state(state)
      """
      pass
  
file_path: ray/python/ray/rllib/env/env_runner.py
imports: import abc
  from typing import Any, Dict, TYPE_CHECKING
  from ray.rllib.utils.actor_manager import FaultAwareApply
  from ray.rllib.utils.annotations import ExperimentalAPI
  from ray.rllib.utils.framework import try_import_tf
label_desc: Use image node_id 5 for calling the EnvRunner obj's underlying member method code with example usage: obj.set_state(state) without return types