class_docstr: Pre-batched training instances for SLCWA of coherent subgraphs.
class_docstr_tok: ["Pre-batched","training","instances","for","SLCWA","of","coherent","subgraphs","."]
docstr: Initialize the instances.
  
  :param kwargs:
      keyword-based parameters passed to :meth:`BaseBatchedSLCWAInstances.__init__`
docstr_tok: ["Initialize","the","instances",".",":","param","kwargs",":","keyword-based","parameters","passed","to",":","meth",":","`","BaseBatchedSLCWAInstances.__init__","`"]
code: def __init__(self, **kwargs):
      """
      Initialize the instances.
  
      :param kwargs:
          keyword-based parameters passed to :meth:`BaseBatchedSLCWAInstances.__init__`
      """
      super().__init__(**kwargs)
      # indexing
      (
          self.degrees,
          self.offset,
          self.neighbors,
      ) = compute_compressed_adjacency_list(
          mapped_triples=self.mapped_triples
      )
  
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 to create a new SubGraphSLCWAInstances object from inherited base classes: BaseBatchedSLCWAInstances with example: obj = SubGraphSLCWAInstances()