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.
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docstr: Returns this EnvRunner's (possibly serialized) current state as a dict.
  
  Returns:
      The current state of this EnvRunner.
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code: def get_state(self) -> Dict[str, Any]:
      """Returns this EnvRunner's (possibly serialized) current state as a dict.
  
      Returns:
          The current state of this EnvRunner.
      """
      # TODO (sven, simon): `Algorithm.save_checkpoint()` will store with
      # this an empty worker state and in `Algorithm.from_checkpoint()`
      # the empty state (not `None`) must be ensured separately. Shall we
      # return here as a default `None`?
      return {}
  
file_path: 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 4 for calling the EnvRunner obj's underlying member method code with example usage: obj.get_state() and returns: dict