class_docstr: Pre-batched training instances for SLCWA of coherent subgraphs.
class_docstr_tok: ["Pre-batched","training","instances","for","SLCWA","of","coherent","subgraphs","."]
code: def iter_triple_ids(self) -> Iterable[List[int]]:  # noqa: D102
      yield from (
          self.subgraph_sample()
          for _ in self.split_workload(n=len(self))
      )
  
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 3 for calling the SubGraphSLCWAInstances obj's underlying member method code with example usage: obj.iter_triple_ids() without return types