code: def test_multiclass_correct_features_relevant(self, y_multi):
      X_multi = pd.DataFrame()
      X_multi["relevant_0"] = np.concatenate(
          [np.zeros(298), np.array([0.01, -0.01])]
      )
      X_multi["relevant_3"] = X_multi["relevant_0"].copy()
      X_multi["relevant_3"][y_multi == 0] = np.random.uniform(2, 3, 100)
      X_multi["relevant_2"] = X_multi["relevant_3"].copy()
      X_multi["relevant_2"][y_multi == 1] = np.random.uniform(
          -2, -1, 100
      )
  
      relevance_table = calculate_relevance_table(
          X_multi,
          y_multi,
          multiclass=True,
          ml_task="classification",
          n_significant=3,
      )
      assert relevance_table.loc["relevant_3", "relevant"]
      assert not relevance_table.loc["relevant_2", "relevant"]
      assert not relevance_table.loc["relevant_0", "relevant"]
  
      # the distributions of all 3 classes under a one vs. rest scheme will be separated enough for
      # this feature to be relevant for predicting 3 classes
      assert relevance_table.loc["relevant_3", "n_significant"] == 3
  
      # due to the distribution of this feature where y_multi == 0 being contained inside the range of
      # y_multi != 0 it will not pass the Mann-Whitney U test under a one vs. rest scheme for that class
      assert relevance_table.loc["relevant_2", "n_significant"] == 2
      assert relevance_table.loc["relevant_0", "n_significant"] == 0
  
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 13 for calling the TestCalculateRelevanceTable obj's underlying member method code with example usage: obj.test_multiclass_correct_features_relevant(y_multi) without return types