code: def test_transform(self):
      X1 = np.array(
          [[3.14, 1.0, 1.0], [3.14, 2.0, np.nan], [3.14, 3.0, 3.0]]
      )  # noqa : matrix legibility
      sdev = np.sqrt(2 / 3)
      # fit
      NPP = NumericalPreprocessingPipeline(
          feat_type={0: "numerical", 1: "numerical", 2: "numerical"}
      )
      NPP.fit_transform(X1)
      # transform
      X2 = np.array(
          [[1.0, 5.0, 8.0], [2.0, 6.0, 9.0], [3.0, 7.0, np.nan]]
      )
      Yt = NPP.transform(X2)
      # imputation, variance_threshold and rescaling are done using the data already
      # fitted, therefore:
      Y2 = np.array(
          [[3 / sdev, 6 / sdev], [4 / sdev, 7 / sdev], [5 / sdev, 0.0]]
      )  # noqa : matrix legibility
      np.testing.assert_array_almost_equal(Yt, Y2)
  
file_path: auto-sklearn/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_numerical.py
imports: import numpy
  from scipy import sparse
  from autosklearn.pipeline.components.data_preprocessing.feature_type_numerical import NumericalPreprocessingPipeline
  import unittest
label_desc: Use image node_id 3 for calling the NumericalPreprocessingPipelineTest obj's underlying member method code with example usage: obj.test_transform() without return types