code: def test_transform(self):
      X1 = np.array([[1, 2, 0], [3, 0, 0], [2, 9, np.nan]])
      Y1 = np.array(
          [
              [1, 0, 0, 0, 1, 0, 0, 1],
              [0, 0, 1, 1, 0, 0, 0, 1],
              [0, 1, 0, 0, 0, 1, 1, 0],
          ]
      )
      X2 = np.array([[2, 2, 1], [3, 0, 0], [2, np.nan, np.nan]])
      Y2 = np.array(
          [
              [0, 1, 0, 0, 1, 0, 0, 0],
              [0, 0, 1, 1, 0, 0, 0, 1],
              [0, 1, 0, 0, 0, 0, 1, 0],
          ]
      )
      X3 = np.array([[3, np.nan, 0], [3, 9, np.nan], [2, 2, 5]])
      Y3 = np.array(
          [
              [0, 0, 1, 0, 0, 0, 0, 1],
              [0, 0, 1, 0, 0, 1, 1, 0],
              [0, 1, 0, 0, 1, 0, 0, 0],
          ]
      )
      # "fit"
      CPPL = CategoricalPreprocessingPipeline()
      CPPL.fit_transform(X1)
      # Transform what was fitted
      Y1t = CPPL.transform(X1)
      np.testing.assert_array_equal(Y1t, Y1)
      # Transform a new dataset with categories not seen during fit
      Y2t = CPPL.transform(X2)
      np.testing.assert_array_equal(Y2t, Y2)
      # And again with yet a different dataset
      Y3t = CPPL.transform(X3)
      np.testing.assert_array_equal(Y3t, Y3)
  
file_path: auto-sklearn/test/test_pipeline/components/data_preprocessing/test_data_preprocessing_categorical.py
imports: import numpy
  from scipy import sparse
  from autosklearn.pipeline.components.data_preprocessing.feature_type_categorical import CategoricalPreprocessingPipeline
  import pytest
  import unittest
label_desc: Use image node_id 3 for calling the CategoricalPreprocessingPipelineTest obj's underlying member method code with example usage: obj.test_transform() without return types