code: def data():
      rng = np.random.RandomState(0)
  
      feature_1 = ["A"] * 10 + ["B"] * 20 + ["C"] * 30
      feature_2 = ["A"] * 40 + ["B"] * 20
      feature_3 = ["A"] * 20 + ["B"] * 20 + ["C"] * 10 + ["D"] * 10
      X = np.array([feature_1, feature_2, feature_3], dtype=object).T
      rng.shuffle(X)
      y = rng.randint(low=0, high=2, size=X.shape[0])
      y_labels = np.array(["not apple", "apple"], dtype=object)
      y = y_labels[y]
      return X, 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 1 for calling a global function with example usage: data() and returns: X,  y