code: def test_fix_multiclass_predict_proba():
      y_proba = np.array(
          [[0.0, 1.0], [0.0, 1.0], [1.0, 0.0], [0.5, 0.5], [0.5, 0.5]],
          dtype=np.float32,
      )
      y_proba_fixed = fix_multiclass_predict_proba(
          y_proba, seen_classes=[0, 2], complete_classes=[0, 1, 2, 3]
      )
      assert y_proba_fixed.dtype == np.float32
      assert np.array_equal(
          y_proba_fixed,
          np.array(
              [
                  [0.0, 0.0, 1.0, 0.0],
                  [0.0, 0.0, 1.0, 0.0],
                  [1.0, 0.0, 0.0, 0.0],
                  [0.5, 0.0, 0.5, 0.0],
                  [0.5, 0.0, 0.5, 0.0],
              ]
          ),
      )
  
file_path: eli5/tests/test_lime_utils.py
imports: from __future__ import absolute_import
  import numpy
  import scipy.sparse
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import mean_absolute_error
  from eli5.lime.utils import fit_proba, fix_multiclass_predict_proba
label_desc: Use image node_id 2 for calling a global function with example usage: test_fix_multiclass_predict_proba() without return types