code: def test_transform_corpus(
      self,
      corpus: ColumnCorpus,
      embeddings: TransformerDocumentEmbeddings,
      cross_augmentation: bool,
      encoding_strategy: EncodingStrategy,
      encoded_entity_pairs: List[Tuple[str, str]],
  ) -> None:
      label_dictionary = corpus.make_label_dictionary("relation")
      model: RelationClassifier = self.build_model(
          embeddings,
          label_dictionary,
          cross_augmentation=cross_augmentation,
          encoding_strategy=encoding_strategy,
      )
      transformed_corpus = model.transform_corpus(corpus)
  
      # Check sentence masking and relation label annotation on
      # training, validation and test dataset (in this test the splits are the same)
      ground_truth: Set[Tuple[str, Tuple[str, ...]]] = {
          # Entity pair permutations of: "Larry Page and Sergey Brin founded Google ."
          (
              f"{encoded_entity_pairs[0][1]} and Sergey Brin founded {encoded_entity_pairs[0][0]} .",
              ("founded_by",),
          ),
          (
              f"Larry Page and {encoded_entity_pairs[1][1]} founded {encoded_entity_pairs[1][0]} .",
              ("founded_by",),
          ),
          # Entity pair permutations of: "Microsoft was founded by Bill Gates ."
          (
              f"{encoded_entity_pairs[2][0]} was founded by {encoded_entity_pairs[2][1]} .",
              ("founded_by",),
          ),
          # Entity pair permutations of: "Konrad Zuse was born in Berlin on 22 June 1910 ."
          (
              f"{encoded_entity_pairs[3][1]} was born in {encoded_entity_pairs[3][0]} on 22 June 1910 .",
              ("place_of_birth",),
          ),
          # Entity pair permutations of: "Joseph Weizenbaum , a professor at MIT , was born in Berlin , Germany."
          (
              f"{encoded_entity_pairs[4][1]} , a professor at MIT , "
              f"was born in {encoded_entity_pairs[4][0]} , Germany .",
              ("place_of_birth",),
          ),
          (
              f"{encoded_entity_pairs[5][1]} , a professor at MIT , "
              f"was born in Berlin , {encoded_entity_pairs[5][0]} .",
              ("place_of_birth",),
          ),
      }
  
      if cross_augmentation:
          # This sentence is only included if we transform the corpus with cross augmentation
          ground_truth.add(
              (
                  f"{encoded_entity_pairs[6][1]} , a professor at {encoded_entity_pairs[6][0]} , "
                  f"was born in Berlin , Germany .",
                  ("O",),
              )
          )
  
      for split in (
          transformed_corpus.train,
          transformed_corpus.dev,
          transformed_corpus.test,
      ):
          self.check_transformation_correctness(split, ground_truth)
  
imports: from operator import itemgetter
  from typing import Dict, List, Optional, Set, Tuple
  import pytest
  from torch.utils.data import Dataset
  from flair.data import Relation, Sentence
  from flair.datasets import ColumnCorpus, DataLoader
  from flair.embeddings import TransformerDocumentEmbeddings
  from flair.models import RelationClassifier
  from flair.models.relation_classifier_model import EncodedSentence, EncodingStrategy, EntityMarker, EntityMarkerPunct, EntityMask, TypedEntityMarker, TypedEntityMarkerPunct, TypedEntityMask
  from tests.model_test_utils import BaseModelTest
label_desc: Use image node_id 8 for calling the TestRelationClassifier obj's underlying member method code with example usage: obj.test_transform_corpus(corpus, embeddings, cross_augmentation, encoding_strategy, encoded_entity_pairs) without return types