class_docstr: Random pre-batched training instances for the sLCWA training loop.
class_docstr_tok: ["Random","pre-batched","training","instances","for","the","sLCWA","training","loop","."]
code: def iter_triple_ids(self) -> Iterable[List[int]]:  # noqa: D102
      yield from data.BatchSampler(
          sampler=data.RandomSampler(
              data_source=self.split_workload(len(self.mapped_triples))
          ),
          batch_size=self.batch_size,
          drop_last=self.drop_last,
      )
  
imports: import math
  from abc import ABC, abstractmethod
  from typing import Callable, Generic, Iterable, Iterator, List, NamedTuple, Optional, Tuple, TypeVar
  import numpy
  import scipy.sparse
  import torch
  from class_resolver import HintOrType, OptionalKwargs
  from torch.utils import data
  from .utils import compute_compressed_adjacency_list
  from ..sampling import NegativeSampler, negative_sampler_resolver
  from ..typing import MappedTriples
label_desc: Use image node_id 1 for calling the BatchedSLCWAInstances obj's underlying member method code with example usage: obj.iter_triple_ids() without return types