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 _slice_size_search(
      self,
      *,
      triples_factory: CoreTriplesFactory,
      batch_size: int,
      sub_batch_size: int,
      supports_sub_batching: bool,
  ):  # noqa: D102
      # Slicing is not possible for sLCWA
      if supports_sub_batching:
          report = "This model supports sub-batching, but it also requires slicing, which is not possible for sLCWA"
      else:
          report = "This model doesn't support sub-batching and slicing is not possible for sLCWA"
      logger.warning(report)
      raise MemoryError(
          "The current model can't be trained on this hardware with these parameters."
      )
  
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 6 for calling the SLCWATrainingLoop obj's underlying member method code with example usage: obj._slice_size_search() without return types