code: def check_transformation_correctness(
      split: Optional[Dataset],
      ground_truth: Set[Tuple[str, Tuple[str, ...]]],
  ) -> None:
      # Ground truth is a set of tuples of (<Sentence Text>, <Relation Label Values>)
      assert split is not None
  
      data_loader = DataLoader(split, batch_size=1)
      assert all(
          isinstance(sentence, EncodedSentence)
          for sentence in map(itemgetter(0), data_loader)
      )
      assert {
          (
              sentence.to_tokenized_string(),
              tuple(
                  label.value
                  for label in sentence.get_labels("relation")
              ),
          )
          for sentence in map(itemgetter(0), data_loader)
      } == 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 7 for calling the TestRelationClassifier obj's underlying member method code with example usage: obj.check_transformation_correctness(split, ground_truth) without return types