code: def test_fit_transform(self):
      X = np.array(
          [[3.14, 1.0, 1.0], [3.14, 2.0, np.nan], [3.14, 3.0, 3.0]]
      )  # noqa : matrix legibility
      # 1st column should be droped due to low variance
      # The 2nd should be be standardized (default rescaling algorithm)
      # The 3rd will get a value imputed by the mean (2.), therefore the
      # transformation here will have the same effect as on the the 2nd column
      sdev = np.sqrt(2 / 3)
      Y1 = np.array(
          [
              [-1 / sdev, -1 / sdev],
              [0.0, 0.0],  # noqa : matrix legibility
              [1 / sdev, 1 / sdev],
          ]
      )  # noqa : matrix legibility
      # dense input
      Yt = NumericalPreprocessingPipeline(
          feat_type={0: "numerical", 1: "numerical", 2: "numerical"}
      ).fit_transform(X)
      np.testing.assert_array_almost_equal(Yt, Y1)
      # sparse input (uses with_mean=False)
      Y2 = np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]]) / sdev
      X_sparse = sparse.csc_matrix(X)
      Yt = NumericalPreprocessingPipeline(
          feat_type={0: "numerical", 1: "numerical", 2: "numerical"}
      ).fit_transform(X_sparse)
      np.testing.assert_array_almost_equal(Yt.todense(), 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 2 for calling the NumericalPreprocessingPipelineTest obj's underlying member method code with example usage: obj.test_fit_transform() without return types