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: Initializes an EnvRunner instance.
  
  Args:
      config: The AlgorithmConfig to use to setup this EnvRunner.
      **kwargs: Forward compatibility kwargs.
docstr_tok: ["Initializes","an","EnvRunner","instance",".","Args",":","config",":","The","AlgorithmConfig","to","use","to","setup","this","EnvRunner",".","*","*","kwargs",":","Forward","compatibility","kwargs","."]
code: def __init__(self, *, config: "AlgorithmConfig", **kwargs):
      """Initializes an EnvRunner instance.
  
      Args:
          config: The AlgorithmConfig to use to setup this EnvRunner.
          **kwargs: Forward compatibility kwargs.
      """
      self.config = config
      super().__init__(**kwargs)
  
      # This eager check is necessary for certain all-framework tests
      # that use tf's eager_mode() context generator.
      if (
          tf1
          and (
              self.config.framework_str == "tf2"
              or config.enable_tf1_exec_eagerly
          )
          and not tf1.executing_eagerly()
      ):
          tf1.enable_eager_execution()
  
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 1 to create a new EnvRunner object from inherited base classes: FaultAwareApply with example: obj = EnvRunner()