code: def test_value_difference_metric_missing_categories(data):
      # Check that we don't get issue when a category is missing between 0
      # n_categories - 1
      X, y = data
  
      encoder = OrdinalEncoder(dtype=np.int32)
      X_encoded = encoder.fit_transform(X)
      n_categories = np.array([len(cat) for cat in encoder.categories_])
  
      # remove a categories that could be between 0 and n_categories
      X_encoded[X_encoded[:, -1] == 1] = 0
      np.testing.assert_array_equal(
          np.unique(X_encoded[:, -1]), [0, 2, 3]
      )
  
      vdm = ValueDifferenceMetric(n_categories=n_categories)
      vdm.fit(X_encoded, y)
  
      for n_cats, proba in zip(n_categories, vdm.proba_per_class_):
          assert proba.shape == (n_cats, len(np.unique(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 6 for calling a global function with example usage: test_value_difference_metric_missing_categories(data) without return types