code: def test_dataset_name(drifted_data_and_model):
      (
          df,
          df2,
          expected,
      ) = util_generate_second_similar_dataframe_and_expected()
  
      result = FeatureLabelCorrelationChange(n_samples=None).run(
          train_dataset=Dataset(
              df, label="label", dataset_name="First"
          ),
          test_dataset=Dataset(
              df2, label="label", dataset_name="Second"
          ),
      )
  
      assert_that(result.display[0].data[0].name, "First")
      assert_that(result.display[0].data[1].name, "Second")
  
imports: import numpy
  import pandas
  from hamcrest import assert_that, calling, close_to, equal_to, greater_than, has_entries, has_length, raises
  from deepchecks.core.errors import DatasetValidationError, DeepchecksNotSupportedError, DeepchecksValueError
  from deepchecks.tabular.checks.data_integrity import FeatureLabelCorrelation
  from deepchecks.tabular.checks.train_test_validation import FeatureLabelCorrelationChange
  from deepchecks.tabular.dataset import Dataset
  from tests.base.utils import equal_condition_result
label_desc: Use image node_id 24 for calling a global function with example usage: test_dataset_name(drifted_data_and_model) without return types