code: def test_featurevalidator_supported_types(input_data_featuretest):
      validator = FeatureValidator()
      validator.fit(input_data_featuretest, input_data_featuretest)
      transformed_X = validator.transform(input_data_featuretest)
      if sparse.issparse(input_data_featuretest):
          assert sparse.issparse(transformed_X)
      elif isinstance(input_data_featuretest, list):
          assert isinstance(transformed_X, pd.DataFrame)
      else:
          assert isinstance(transformed_X, type(input_data_featuretest))
      assert np.shape(input_data_featuretest) == np.shape(transformed_X)
      assert validator._is_fitted
  
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 2 for calling a global function with example usage: test_featurevalidator_supported_types(input_data_featuretest) without return types