code: def test_target_real_calls_correct_tests(
      self,
      significance_test_feature_binary_mock,
      significance_test_feature_real_mock,
      X,
      y_real,
  ):
      significance_test_feature_binary_mock.return_value = 0.5
      significance_test_feature_real_mock.return_value = 0.7
  
      relevance_table = calculate_relevance_table(X, y_real, n_jobs=0)
  
      assert 0.5 == relevance_table.loc["feature_binary"].p_value
      assert 0.7 == relevance_table.loc["feature_real"].p_value
  
      assert significance_test_feature_binary_mock.call_count == 1
      pd.testing.assert_series_equal(
          significance_test_feature_binary_mock.call_args[0][0],
          X["feature_binary"],
      )
      pd.testing.assert_series_equal(
          significance_test_feature_binary_mock.call_args[1]["y"],
          y_real,
      )
  
      assert significance_test_feature_real_mock.call_count == 1
      pd.testing.assert_series_equal(
          significance_test_feature_real_mock.call_args[0][0],
          X["feature_real"],
      )
      pd.testing.assert_series_equal(
          significance_test_feature_real_mock.call_args[1]["y"], y_real
      )
  
imports: import warnings
  import mock
  import numpy
  import pandas
  import pytest
  from tsfresh.feature_selection.relevance import calculate_relevance_table, combine_relevance_tables, get_feature_type, infer_ml_task
label_desc: Use image node_id 8 for calling the TestCalculateRelevanceTable obj's underlying member method code with example usage: obj.test_target_real_calls_correct_tests(significance_test_feature_binary_mock, significance_test_feature_real_mock, X, y_real) without return types