code: def test_no_new_category_after_fit():
      """
      This test makes sure that we can actually pass new categories to the estimator
      without throwing an error
      """
      # Then make sure we catch categorical extra categories
      x = pd.DataFrame(
          {"A": [1, 2, 3, 4], "B": [5, 6, 7, 8]}, dtype="category"
      )
      validator = FeatureValidator()
      validator.fit(x)
      x["A"] = x["A"].apply(lambda x: x * x)
      validator.transform(x)
  
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 7 for calling a global function with example usage: test_no_new_category_after_fit() without return types