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: Fetch next results produced by ``session.report()`` from each worker.
  
  Assumes ``start_training`` has already been called.
  
  Returns:
      A list of dictionaries of values passed to ``session.report()`` from
          each worker. Each item corresponds to an intermediate result
          a single worker. If there are no more items to fetch,
          returns None.
docstr_tok: ["Fetch","next","results","produced","by","``","session.report","(",")","``","from","each","worker",".","Assumes","``","start_training","``","has","already","been","called",".","Returns",":","A","list","of","dictionaries","of","values","passed","to","``","session.report","(",")","``","from","each","worker",".","Each","item","corresponds","to","an","intermediate","result","a","single","worker",".","If","there","are","no","more","items","to","fetch",",","returns","None","."]
code: def _fetch_next_result(self) -> Optional[List[Dict]]:
      """Fetch next results produced by ``session.report()`` from each worker.
  
      Assumes ``start_training`` has already been called.
  
      Returns:
          A list of dictionaries of values passed to ``session.report()`` from
              each worker. Each item corresponds to an intermediate result
              a single worker. If there are no more items to fetch,
              returns None.
      """
      results = self._backend_executor.get_next_results()
      if results is None:
          return None
      assert all(
          isinstance(result, _TrainingResult) for result in results
      )
      return results
  
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 6 for calling the TrainingIterator obj's underlying member method code with example usage: obj._fetch_next_result() and returns: results, None