class_docstr: Top-level loop where prediction starts.
class_docstr_tok: ["Top-level","loop","where","prediction","starts","."]
code: def return_predictions(
      self, return_predictions: Optional[bool] = None
  ) -> None:
      # Strategies that spawn or fork don't support returning predictions
      return_supported = not isinstance(
          self.trainer.strategy.launcher, _MultiProcessingLauncher
      )
      if return_predictions and not return_supported:
          raise MisconfigurationException(
              "`return_predictions` should be set to `False` when using the strategies that spawn or fork."
              f" Found {return_predictions} with strategy {type(self.trainer.strategy)}."
          )
      # For strategies that support it, `return_predictions` is True by default unless user decide otherwise.
      self._return_predictions = (
          return_supported
          if return_predictions is None
          else return_predictions
      )
  
imports: from collections import OrderedDict
  from typing import Any, Iterator, List, Optional, Union
  import torch
  from lightning_utilities import WarningCache
  import lightning.pytorch
  from lightning.fabric.utilities import move_data_to_device
  from lightning.pytorch.callbacks import BasePredictionWriter
  from lightning.pytorch.loops.fetchers import _DataFetcher, _DataLoaderIterDataFetcher
  from lightning.pytorch.loops.loop import _Loop
  from lightning.pytorch.loops.progress import _Progress
  from lightning.pytorch.loops.utilities import _no_grad_context, _select_data_fetcher, _verify_dataloader_idx_requirement
  from lightning.pytorch.overrides.distributed import _IndexBatchSamplerWrapper
  from lightning.pytorch.strategies.launchers import _MultiProcessingLauncher
  from lightning.pytorch.trainer import call
  from lightning.pytorch.trainer.connectors.data_connector import _check_dataloader_iterable, _DataLoaderSource, _parse_num_batches, _process_dataloader, _request_dataloader
  from lightning.pytorch.trainer.states import RunningStage, TrainerFn
  from lightning.pytorch.utilities.combined_loader import CombinedLoader
  from lightning.pytorch.utilities.data import has_len_all_ranks
  from lightning.pytorch.utilities.exceptions import MisconfigurationException
  from lightning.pytorch.utilities.model_helpers import _ModuleMode
  from lightning.pytorch.utilities.signature_utils import is_param_in_hook_signature
  from lightning.pytorch.utilities.types import _PREDICT_OUTPUT
label_desc: Use image node_id 3 for calling the _PredictionLoop obj's underlying member method code with example usage: obj.return_predictions(return_predictions) without return types