code: def test_more_than_1_correlated_group(df_correlated_double):
      transformer = DropCorrelatedFeatures(
          variables=None, method="pearson", threshold=0.6
      )
      X = transformer.fit_transform(df_correlated_double)
  
      # expected result
      df = df_correlated_double.drop(
          ["var_6", "var_7", "var_8", "var_9"], axis=1
      )
  
      # test fit attrs
      assert transformer.features_to_drop_ == {
          "var_6",
          "var_7",
          "var_8",
          "var_9",
      }
      assert transformer.correlated_feature_sets_ == [
          {"var_0", "var_8"},
          {"var_4", "var_6", "var_7", "var_9"},
      ]
      # test transform output
      pd.testing.assert_frame_equal(X, df)
  
file_path: feature_engine/tests/test_selection/test_drop_correlated_features.py
imports: import pandas
  import pytest
  from sklearn.datasets import make_classification
  from feature_engine.selection import DropCorrelatedFeatures
label_desc: Use image node_id 5 for calling a global function with example usage: test_more_than_1_correlated_group(df_correlated_double) without return types