class_docstr: This combinator keeps both graphs as disconnected components.
class_docstr_tok: ["This","combinator","keeps","both","graphs","as","disconnected","components","."]
code: def process(
      self,
      mapped_triples: MappedTriples,
      alignment: torch.LongTensor,
      offsets: torch.LongTensor,
  ) -> ProcessedTuple:  # noqa: D102
      return ProcessedTuple(
          mapped_triples,
          alignment,
          dict(
              entity_offsets=offsets[:, 0],
              relation_offsets=offsets[:, 1],
          ),
      )
  
imports: import logging
  from abc import ABC, abstractmethod
  from collections import defaultdict
  from typing import Any, ClassVar, DefaultDict, Dict, Iterable, Mapping, NamedTuple, Optional, Sequence, Set, Tuple, Union
  import numpy
  import pandas
  import torch
  from class_resolver import ClassResolver
  from pandas.api.types import is_numeric_dtype, is_string_dtype
  from ...triples import CoreTriplesFactory, TriplesFactory
  from ...typing import COLUMN_HEAD, COLUMN_TAIL, EA_SIDE_LEFT, EA_SIDE_RIGHT, EA_SIDES, MappedTriples, TargetColumn
  from ...utils import format_relative_comparison, get_connected_components
label_desc: Use image node_id 1 for calling the DisjointGraphPairCombinator obj's underlying member method code with example usage: obj.process(mapped_triples, alignment, offsets) and returns: ProcessedTuple