code: def test_value_difference_metric_categories_error(data):
      # Check that we raise an error if n_categories is inconsistent with the
      # number of features in X
      X, y = data
  
      encoder = OrdinalEncoder(dtype=np.int32)
      X_encoded = encoder.fit_transform(X)
      n_categories = [1, 2]
  
      vdm = ValueDifferenceMetric(n_categories=n_categories)
      err_msg = (
          "The length of n_categories is not consistent with the number"
      )
      with pytest.raises(ValueError, match=err_msg):
          vdm.fit(X_encoded, y)
  
file_path: imbalanced-learn/imblearn/metrics/tests/test_pairwise.py
imports: import numpy
  import pytest
  from sklearn.exceptions import NotFittedError
  from sklearn.preprocessing import LabelEncoder, OrdinalEncoder
  from sklearn.utils._testing import _convert_container
  from imblearn.metrics.pairwise import ValueDifferenceMetric
label_desc: Use image node_id 5 for calling a global function with example usage: test_value_difference_metric_categories_error(data) without return types