code: def test_transform_with_sparse_column_with_negative_labels(self):
      X = sparse.csr_matrix([[0], [-1]])
      CategoricalPreprocessingPipeline().fit_transform(X)
  
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 5 for calling the CategoricalPreprocessingPipelineTest obj's underlying member method code with example usage: obj.test_transform_with_sparse_column_with_negative_labels() without return types