code: def test_targetvalidator_fitontypeA_transformtypeB(
      input_data_targettest,
  ):
      """
      Check if we can fit in a given type (numpy) yet transform
      if the user changes the type (pandas then)
  
      This is problematic only in the case we create an encoder
      """
      validator = TargetValidator(is_classification=True)
      validator.fit(input_data_targettest)
      if isinstance(input_data_targettest, pd.DataFrame):
          complementary_type = input_data_targettest.to_numpy()
      elif isinstance(input_data_targettest, pd.Series):
          complementary_type = pd.DataFrame(input_data_targettest)
      elif isinstance(input_data_targettest, np.ndarray):
          complementary_type = pd.DataFrame(input_data_targettest)
      elif isinstance(input_data_targettest, list):
          complementary_type = pd.DataFrame(input_data_targettest)
      validator.transform(complementary_type)
  
imports: import numpy
  import pandas
  import sklearn.datasets
  import sklearn.model_selection
  from pandas.api.types import is_bool_dtype, is_numeric_dtype
  from scipy import sparse
  from sklearn.utils.multiclass import type_of_target
  from autosklearn.data.target_validator import TargetValidator
  import pytest
label_desc: Use image node_id 9 for calling a global function with example usage: test_targetvalidator_fitontypeA_transformtypeB(input_data_targettest) without return types