class_docstr: A method for splitting triples.
class_docstr_tok: ["A","method","for","splitting","triples","."]
docstr: Split triples into clean groups.
  
  This method partitions the triples, i.e., each triple is in exactly one group. Moreover, it ensures that
  the first group contains all entities at least once.
  
  :param mapped_triples: shape: (n, 3)
      the ID-based triples
  :param sizes:
      the absolute number of triples for each split part.
  :param random_state:
      the random state used for splitting
  
  :return:
      a sequence of ID-based triples for each split part. the absolute may be different to ensure the constraint.
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code: def split_absolute_size(
      self,
      mapped_triples: MappedTriples,
      sizes: Sequence[int],
      random_state: torch.Generator,
  ) -> Sequence[MappedTriples]:
      """Split triples into clean groups.
  
      This method partitions the triples, i.e., each triple is in exactly one group. Moreover, it ensures that
      the first group contains all entities at least once.
  
      :param mapped_triples: shape: (n, 3)
          the ID-based triples
      :param sizes:
          the absolute number of triples for each split part.
      :param random_state:
          the random state used for splitting
  
      :return:
          a sequence of ID-based triples for each split part. the absolute may be different to ensure the constraint.
      """
      raise NotImplementedError
  
imports: import logging
  import typing
  from abc import abstractmethod
  from typing import Collection, Optional, Sequence, Set, Tuple, Type, Union
  import numpy
  import pandas
  import torch
  from class_resolver.api import ClassResolver, HintOrType
  from ..constants import COLUMN_LABELS
  from ..typing import LABEL_HEAD, LABEL_RELATION, LABEL_TAIL, MappedTriples, Target, TorchRandomHint
  from ..utils import ensure_torch_random_state
label_desc: Use image node_id 1 for calling the Splitter obj's underlying member method code with example usage: obj.split_absolute_size(mapped_triples, sizes, random_state) without return types