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: If this Actor is deleted, clears all resources used by it.
docstr_tok: ["If","this","Actor","is","deleted",",","clears","all","resources","used","by","it","."]
code: def __del__(self) -> None:
      """If this Actor is deleted, clears all resources used by it."""
      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 7 for calling the EnvRunner obj's underlying member method code with example usage: obj.__del__() without return types