code: def _calculate_relevance_table_for_implicit_target(
      table_real,
      table_binary,
      X,
      test_real_feature,
      test_binary_feature,
      hypotheses_independent,
      fdr_level,
      map_function,
  ):
      table_real["p_value"] = pd.Series(
          map_function(
              test_real_feature,
              [X[feature] for feature in table_real.index],
          ),
          index=table_real.index,
      )
      table_binary["p_value"] = pd.Series(
          map_function(
              test_binary_feature,
              [X[feature] for feature in table_binary.index],
          ),
          index=table_binary.index,
      )
      relevance_table = pd.concat([table_real, table_binary])
      method = "fdr_bh" if hypotheses_independent else "fdr_by"
      relevance_table["relevant"] = multipletests(
          relevance_table.p_value, fdr_level, method
      )[0]
      return relevance_table.sort_values("p_value")
  
file_path: tsfresh/tsfresh/feature_selection/relevance.py
imports: import warnings
  from functools import partial, reduce
  from multiprocessing import Pool
  import numpy
  import pandas
  from statsmodels.stats.multitest import multipletests
  from tsfresh import defaults
  from tsfresh.feature_selection.significance_tests import target_binary_feature_binary_test, target_binary_feature_real_test, target_real_feature_binary_test, target_real_feature_real_test
  from tsfresh.utilities.distribution import initialize_warnings_in_workers
label_desc: Use image node_id 2 for calling a global function with example usage: _calculate_relevance_table_for_implicit_target(table_real, table_binary, X, test_real_feature, test_binary_feature, hypotheses_independent, fdr_level, map_function) and returns: relevance_table