class_docstr: An iterator over Train results. Returned by ``trainer.run_iterator``.
class_docstr_tok: ["An","iterator","over","Train","results",".","Returned","by","``","trainer.run_iterator","``","."]
docstr: Finish training and return final results. Propagate any exceptions.
  
  Blocks until training is finished on all workers.
  
  Assumes `start_training` has already been called.
  
  Returns:
      A list of return values from calling ``train_func`` on each worker.
          Each item corresponds to the return value from a single worker.
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code: def _finish_training(self):
      """Finish training and return final results. Propagate any exceptions.
  
      Blocks until training is finished on all workers.
  
      Assumes `start_training` has already been called.
  
      Returns:
          A list of return values from calling ``train_func`` on each worker.
              Each item corresponds to the return value from a single worker.
      """
      return self._backend_executor.finish_training()
  
imports: import logging
  from pathlib import Path
  from typing import Any, Callable, Dict, List, Optional, TypeVar, Union
  from ray.air._internal.util import StartTraceback
  from ray.data import Dataset
  from ray.train import Checkpoint, DataConfig
  from ray.train._internal.backend_executor import BackendExecutor, InactiveWorkerGroupError, TrainBackendError, TrainingWorkerError
  from ray.train._internal.session import _TrainingResult, _TrainSession, get_session
  from ray.train._internal.utils import ActorWrapper
  from ray.train.backend import BackendConfig
  from ray.train.base_trainer import BaseTrainer, GenDataset, TrainingFailedError
  from ray.util.annotations import DeveloperAPI
label_desc: Use image node_id 7 for calling the TrainingIterator obj's underlying member method code with example usage: obj._finish_training() and returns: self