class_docstr: A training loop that uses the stochastic local closed world assumption training approach.
  
  [ruffinelli2020]_ call the sLCWA ``NegSamp`` in their work.
class_docstr_tok: ["A","training","loop","that","uses","the","stochastic","local","closed","world","assumption","training","approach",".","[","ruffinelli2020","]","_","call","the","sLCWA","``","NegSamp","``","in","their","work","."]
code: def _process_batch(
      self,
      batch: SLCWABatch,
      start: int,
      stop: int,
      label_smoothing: float = 0.0,
      slice_size: Optional[int] = None,
  ) -> torch.FloatTensor:  # noqa: D102
      return self._process_batch_static(
          model=self.model,
          loss=self.loss,
          mode=self.mode,
          batch=batch,
          start=start,
          stop=stop,
          label_smoothing=label_smoothing,
          slice_size=slice_size,
      )
  
imports: import logging
  from typing import Optional
  import torch.utils.data
  from class_resolver import HintOrType, OptionalKwargs
  from torch.utils.data import DataLoader
  from .training_loop import TrainingLoop
  from ..losses import Loss
  from ..models.base import Model
  from ..sampling import NegativeSampler
  from ..triples import CoreTriplesFactory
  from ..triples.instances import SLCWABatch, SLCWASampleType
  from ..typing import InductiveMode
label_desc: Use image node_id 5 for calling the SLCWATrainingLoop obj's underlying member method code with example usage: obj._process_batch(batch, start, stop, label_smoothing, slice_size) and returns: self