code: def test_zero_variance():
      """Test for column with zero variance."""
      X = np.array(
          [
              [-0.626, 0.659, 1.0],
              [0.184, -1.641, 1.0],
              [-0.836, 0.975, 1.0],
              [1.595, 1.477, 1.0],
          ]
      )
  
      coords_expected = np.array(
          [
              [0.4736931, 0.57202103, 0],
              [0.35209305, -0.22820652, 0],
              [0.45632521, 0.70405059, 0],
              [1.3830473, 0.2952629, 0],
          ]
      )
  
      basis_expected = np.array(
          [
              [0.8391551, -0.5438922, 0.0],
              [0.5438922, 0.8391551, 0.0],
              [0.0, 0.0, 1.0],
          ]
      )
  
      scaler = MinMaxScaler()
      X = scaler.fit_transform(X)
      model = DOBIN(k=1, frac=0.5)
      fitted_model = model.fit(X)
      coords_actual = fitted_model._coords
      basis_actual = fitted_model._basis
  
      assert np.allclose(coords_expected, coords_actual)
      assert np.allclose(basis_expected, basis_actual)
  
imports: import numpy
  from sklearn.preprocessing import MinMaxScaler, RobustScaler
  from sktime.transformations.series.dobin import DOBIN
label_desc: Use image node_id 4 for calling a global function with example usage: test_zero_variance() without return types