code: def test_between_all_groups():
      data = np.array(
          [
              [0, 1],
              [0, 0],
              [1, 0],
              [1, 1],
              [1, 0],
              [1, 0],
              [2, 1],
              [2, 0],
              [2, 1],
              [2, 1],
          ]
      )
      pred = data.copy()
      pred[[3, 9], -1] = 0
      pred[[4, 5], -1] = 1
      df = pd.DataFrame(data, columns=["feat", "label"])
      df2 = pd.DataFrame(pred, columns=["feat", "label"])
      bld = BinaryLabelDataset(
          df=df,
          label_names=["label"],
          protected_attribute_names=["feat"],
      )
      bld2 = BinaryLabelDataset(
          df=df2,
          label_names=["label"],
          protected_attribute_names=["feat"],
      )
      cm = ClassificationMetric(bld, bld2)
  
      b = np.array(
          [1, 1, 1.25, 1.25, 1.25, 1.25, 0.75, 0.75, 0.75, 0.75]
      )
      assert (
          cm.between_all_groups_generalized_entropy_index()
          == 1 / 20 * np.sum(b**2 - 1)
      )
  
file_path: AIF360/tests/test_classification_metric.py
imports: import numpy
  import pandas
  from aif360.datasets import BinaryLabelDataset
  from aif360.datasets.multiclass_label_dataset import MulticlassLabelDataset
  from aif360.metrics import ClassificationMetric
label_desc: Use image node_id 3 for calling a global function with example usage: test_between_all_groups() without return types