code: def test_transform_with_unknown_value(self):
      # fit_data: this is going to be used to fit.
      # note that 0 is no category because the data here is sparse.
      fit_data = np.array(
          ((0, 1, 2, 3, 4, 5), (0, 1, 2, 3, 4, 5))
      ).transpose()
      fds = scipy.sparse.csr_matrix(fit_data)
      ohe = SparseOneHotEncoder()
      ohe.fit(fds)
      # transf_data: this is going to be used in a transform call.
      # Note that transf_data has categories not seen at the fit.
      # Unseen categories are ignored (encoded just with zeros).
      transf_data = np.array(((0, 1, 2, 6), (0, 1, 6, 7))).transpose()
      tds = scipy.sparse.csr_matrix(transf_data)
      output = ohe.transform(tds).todense()
      # From tds, just 3 categories (1 and 2 in the 1st feature and 1 in the 2nd
      # feature) have been seen during fit, therefore:
      self.assertEqual(3, np.sum(output))
  
file_path: auto-sklearn/test/test_pipeline/implementations/test_SparseOneHotEncoder.py
imports: import numpy
  import scipy.sparse
  import sklearn.datasets
  import sklearn.model_selection
  import sklearn.pipeline
  import sklearn.tree
  from sklearn.impute import SimpleImputer
  from sklearn.tree import DecisionTreeClassifier
  from autosklearn.pipeline.implementations.CategoryShift import CategoryShift
  from autosklearn.pipeline.implementations.SparseOneHotEncoder import SparseOneHotEncoder
  import unittest
label_desc: Use image node_id 6 for calling the TestSparseOneHotEncoder obj's underlying member method code with example usage: obj.test_transform_with_unknown_value() without return types