code: def test_lower_threshold(df_correlated_single):
      transformer = DropCorrelatedFeatures(
          variables=None, method="pearson", threshold=0.6
      )
      X = transformer.fit_transform(df_correlated_single)
  
      # expected result
      df = df_correlated_single.drop(["var_2", "var_4"], axis=1)
  
      # test init params
      assert transformer.method == "pearson"
      assert transformer.threshold == 0.6
  
      # test fit attrs
      assert transformer.features_to_drop_ == {"var_2", "var_4"}
      assert transformer.correlated_feature_sets_ == [
          {"var_1", "var_2", "var_4"}
      ]
      # 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 4 for calling a global function with example usage: test_lower_threshold(df_correlated_single) without return types