code: def test_classification_workflow(self):
      X, y = sklearn.datasets.fetch_openml(
          data_id=24, as_frame=False, return_X_y=True
      )
      print(type(X))
  
      (
          X_train,
          X_test,
          y_train,
          y_test,
      ) = sklearn.model_selection.train_test_split(
          X, y, random_state=3, train_size=0.5, test_size=0.5
      )
  
      X_train = scipy.sparse.csc_matrix(X_train)
      X_test = scipy.sparse.csc_matrix(X_test)
  
      pipeline = sklearn.pipeline.Pipeline(
          (
              ("shift", CategoryShift()),
              (
                  "imput",
                  SimpleImputer(strategy="constant", fill_value=2),
              ),
              ("ohe", SparseOneHotEncoder()),
              ("tree", DecisionTreeClassifier(random_state=1)),
          )
      )
  
      pipeline.fit(X_train, y_train)
      pred_train = pipeline.predict(X_train)
      self.assertTrue((pred_train == y_train).all())
      # With an incorrect copy operation the OneHotEncoder would rearrange
      # the data in such a way that the accuracy would drop to 66%
      pred_test = pipeline.predict(X_test)
      self.assertTrue((pred_test == y_test).all())
  
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 7 for calling the TestSparseOneHotEncoder obj's underlying member method code with example usage: obj.test_classification_workflow() without return types