code: def test_pca_reduction():
      """Test when n_obs is less than n_dim and PCA is applied initially."""
      X = np.array(
          [
              [
                  1.4374e01,
                  6.5900e00,
                  1.1520e00,
                  -1.2400e-01,
                  -1.6000e-02,
                  9.1900e-01,
              ],
              [
                  3.7000e-02,
                  -1.6409e01,
                  -6.1100e-01,
                  -4.4300e-01,
                  9.4400e-01,
                  7.8200e-01,
              ],
              [
                  -1.6700e-01,
                  9.7490e00,
                  3.0240e00,
                  2.2500e-01,
                  8.2100e-01,
                  7.5000e-02,
              ],
              [
                  -1.3405e01,
                  1.4766e01,
                  7.8000e-01,
                  -9.0000e-03,
                  5.9400e-01,
                  -1.9890e00,
              ],
          ]
      )
  
      X_expected = np.array(
          [
              [0.2175, -0.805, 0.2853, 0.0],
              [0.9628, 0.4449, -0.1128, -0.0],
              [-0.5299, -0.1585, -0.5455, -0.0],
              [-0.6503, 0.5186, 0.3729, 0.0],
          ]
      )
  
      scaler = MinMaxScaler()
      X = scaler.fit_transform(X)
      model = DOBIN(k=2, frac=0.5)
      fitted_model = model.fit(X)
      X_actual = fitted_model._X_pca
  
      assert np.allclose(abs(X_expected), abs(X_actual), rtol=0.001)
  
imports: import numpy
  from sklearn.preprocessing import MinMaxScaler, RobustScaler
  from sktime.transformations.series.dobin import DOBIN
label_desc: Use image node_id 3 for calling a global function with example usage: test_pca_reduction() without return types