code: def swap_index_triples(
      mapped_triples: MappedTriples,
      dense_map: torch.LongTensor,
      index: TargetColumn,
  ) -> MappedTriples:
      """
      Return triples where some indices in the index column are swapped.
  
      :param mapped_triples: shape: (m, 3)
          the id-based triples
      :param dense_map: shape: (n,)
          a dense map between IDs. Contains `-1` for missing entries
      :param index:
          the index, a number between 0 (incl) and 3 (excl).
  
      :return:
          all triples which contain a key at the `index` position, where the key has been replaced by the corresponding
          value in the dense map
      """
      # determine swapping partner
      trans = dense_map[mapped_triples[:, index]]
      # only keep triples where we have a swapping partner
      mask = trans >= 0
      mapped_triples = mapped_triples[mask].clone()
      # replace by swapping partner
      mapped_triples[:, index] = trans[mask]
      return mapped_triples
  
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 5 for calling a global function with example usage: swap_index_triples(mapped_triples, dense_map, index) and returns: mapped_triples