code: def test_sparse_output_is_csr(input_data_featuretest):
      validator = FeatureValidator()
      validator.fit(input_data_featuretest, input_data_featuretest)
      transformed_X = validator.transform(input_data_featuretest)
      assert sparse.issparse(transformed_X)
      assert isinstance(transformed_X, sparse.csr_matrix)
  
imports: import numpy
  import pandas
  import sklearn.datasets
  import sklearn.model_selection
  from pandas.api.types import is_categorical_dtype, is_numeric_dtype, is_string_dtype
  from scipy import sparse
  from autosklearn.data.feature_validator import FeatureValidator
  import pytest
label_desc: Use image node_id 10 for calling a global function with example usage: test_sparse_output_is_csr(input_data_featuretest) without return types