class_docstr: Top-level loop where prediction starts.
class_docstr_tok: ["Top-level","loop","where","prediction","starts","."]
docstr: Helper method to build args for `predict_step`.
docstr_tok: ["Helper","method","to","build","args","for","`","predict_step","`","."]
code: def _build_step_args_from_hook_kwargs(
      self, hook_kwargs: OrderedDict, step_hook_name: str
  ) -> tuple:
      """Helper method to build args for `predict_step`."""
      kwargs = hook_kwargs.copy()
      step_hook_fx = getattr(
          self.trainer.lightning_module, step_hook_name
      )
      if not is_param_in_hook_signature(
          step_hook_fx, "batch_idx", min_args=2
      ):
          kwargs.pop("batch_idx", None)
      return tuple(kwargs.values())
  
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 15 for calling the _PredictionLoop obj's underlying member method code with example usage: obj._build_step_args_from_hook_kwargs(hook_kwargs, step_hook_name) and returns: tuple