class_docstr: Collect experiences from user environment using Connectors.
docstr: Samples and yields training episodes continuously.
  
  Yields:
      Object containing state, action, reward, terminal condition,
      and other fields as dictated by `policy`.
docstr_tok: ["Samples","and","yields","training","episodes","continuously",".","Yields",":","Object","containing","state",",","action",",","reward",",","terminal","condition",",","and","other","fields","as","dictated","by","`","policy","`","."]
code: def run(self) -> Iterator[SampleBatchType]:
      """Samples and yields training episodes continuously.
  
      Yields:
          Object containing state, action, reward, terminal condition,
          and other fields as dictated by `policy`.
      """
      while True:
          outputs = self.step()
          for o in outputs:
              yield o
  
imports: from collections import defaultdict
  import logging
  import time
  import tree
  from typing import TYPE_CHECKING, Dict, Iterator, List, Optional, Set, Tuple, Union
  import numpy
  from ray.rllib.env.base_env import ASYNC_RESET_RETURN, BaseEnv
  from ray.rllib.env.external_env import ExternalEnvWrapper
  from ray.rllib.env.wrappers.atari_wrappers import MonitorEnv, get_wrapper_by_cls
  from ray.rllib.evaluation.collectors.simple_list_collector import _PolicyCollectorGroup
  from ray.rllib.policy.rnn_sequencing import pad_batch_to_sequences_of_same_size
  from ray.rllib.evaluation.episode_v2 import EpisodeV2
  from ray.rllib.evaluation.metrics import RolloutMetrics
  from ray.rllib.models.preprocessors import Preprocessor
  from ray.rllib.policy.policy import Policy
  from ray.rllib.policy.sample_batch import MultiAgentBatch, SampleBatch, concat_samples
  from ray.rllib.utils.annotations import DeveloperAPI
  from ray.rllib.utils.filter import Filter
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.spaces.space_utils import unbatch, get_original_space
  from ray.rllib.utils.typing import ActionConnectorDataType, AgentConnectorDataType, AgentID, EnvActionType, EnvID, EnvInfoDict, EnvObsType, MultiAgentDict, MultiEnvDict, PolicyID, PolicyOutputType, SampleBatchType, StateBatches, TensorStructType
  from ray.util.debug import log_once
label_desc: Use image node_id 5 for calling the EnvRunnerV2 obj's underlying member method code with example usage: obj.run() without return types