code: def test_linear_dependence_pd():
      X = np.array(
          [
              [
                  0,
                  0,
                  1,
                  1,
              ],
              [
                  1,
                  1,
                  2,
                  2,
              ],
              [
                  0.1,
                  0.2,
                  1.2,
                  1.1,
              ],
          ]
      ).T
  
      df = pd.DataFrame(X, columns=["a", "b", "c"])
  
      X_tfm = (
          CorrelationRemover(sensitive_feature_ids=["a"])
          .fit(df)
          .transform(df)
      )
      assert X_tfm.shape[1] == 2
      assert np.allclose(X_tfm[:, 0], 1.5)
  
imports: import numpy
  import pandas
  from sklearn.utils.estimator_checks import parametrize_with_checks
  from fairlearn.preprocessing import CorrelationRemover
label_desc: Use image node_id 3 for calling a global function with example usage: test_linear_dependence_pd() without return types