class_docstr: A PyTorch Lightning module for training a model with sLCWA training loop.
class_docstr_tok: ["A","PyTorch","Lightning","module","for","training","a","model","with","sLCWA","training","loop","."]
code: def _dataloader(
      self, triples_factory: CoreTriplesFactory, shuffle: bool = False
  ) -> torch.utils.data.DataLoader:  # noqa: D102
      return torch.utils.data.DataLoader(
          dataset=triples_factory.create_slcwa_instances(
              batch_size=self.batch_size,
              # TODO:
              # shuffle=shuffle,
              # drop_last=drop_last,
              negative_sampler=self.negative_sampler,
              negative_sampler_kwargs=self.negative_sampler_kwargs,
              # sampler=sampler,
          ),
          # shuffle=shuffle,
          # disable automatic batching in data loader
          sampler=None,
          batch_size=None,
      )
  
imports: from abc import abstractmethod
  from typing import Optional
  import click
  import pytorch_lightning
  import torch
  import torch.utils.data
  from class_resolver import ClassResolver, HintOrType, OptionalKwargs
  from pykeen.datasets import dataset_resolver, get_dataset
  from pykeen.datasets.base import Dataset
  from pykeen.losses import Loss, loss_resolver
  from pykeen.models import Model, model_resolver
  from pykeen.models.cli import options
  from pykeen.optimizers import optimizer_resolver
  from pykeen.sampling import NegativeSampler
  from pykeen.training import LCWATrainingLoop, SLCWATrainingLoop
  from pykeen.triples.triples_factory import CoreTriplesFactory
  from pykeen.typing import InductiveMode, OneOrSequence
label_desc: Use image node_id 3 for calling the SLCWALitModule obj's underlying member method code with example usage: obj._dataloader(triples_factory, shuffle) and returns: torch