class_docstr: Pre-batched training instances for the sLCWA training loop.
  
  .. note ::
      this class is intended to be used with automatic batching disabled, i.e., both parameters `batch_size` and
      `batch_sampler` of torch.utils.data.DataLoader` are set to `None`.
class_docstr_tok: ["Pre-batched","training","instances","for","the","sLCWA","training","loop",".","..","note",":",":","this","class","is","intended","to","be","used","with","automatic","batching","disabled",",","i.e.",",","both","parameters","`","batch_size","`","and","`","batch_sampler","`","of","torch.utils.data.DataLoader","`","are","set","to","`","None","`","."]
docstr: Iterate over batches of IDs of positive triples.
docstr_tok: ["Iterate","over","batches","of","IDs","of","positive","triples","."]
code: def iter_triple_ids(self) -> Iterable[List[int]]:
      """Iterate over batches of IDs of positive triples."""
      raise NotImplementedError
  
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 4 for calling the BaseBatchedSLCWAInstances obj's underlying member method code with example usage: obj.iter_triple_ids() without return types