code: def test_featurevalidator_fitontypeA_transformtypeB(
      input_data_featuretest,
  ):
      """
      Check if we can fit in a given type (numpy) yet transform
      if the user changes the type (pandas then)
      """
      validator = FeatureValidator()
      validator.fit(input_data_featuretest, input_data_featuretest)
      if isinstance(input_data_featuretest, pd.DataFrame):
          complementary_type = input_data_featuretest.to_numpy()
      elif isinstance(input_data_featuretest, np.ndarray):
          complementary_type = pd.DataFrame(input_data_featuretest)
      elif isinstance(input_data_featuretest, list):
          complementary_type = pd.DataFrame(input_data_featuretest)
      elif sparse.issparse(input_data_featuretest):
          complementary_type = sparse.csr_matrix(
              input_data_featuretest.todense()
          )
      else:
          raise ValueError(type(input_data_featuretest))
      transformed_X = validator.transform(complementary_type)
      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 4 for calling a global function with example usage: test_featurevalidator_fitontypeA_transformtypeB(input_data_featuretest) without return types