code: def test_multiple_classes():
      gt_label = np.array(
          [
              [1.0, 0.0, 0.0],
              [0.0, 0.0, 1.0],
              [1.0, 0.0, 0.0],
              [0.0, 0.0, 1.0],
              [0.0, 1.0, 0.0],
              [0.0, 1.0, 0.0],
              [1.0, 0.0, 0.0],
              [1.0, 0.0, 0.0],
              [0.0, 0.0, 1.0],
              [0.0, 1.0, 0.0],
          ]
      )
      preds = np.array(
          [
              [0.0, 0.0, 1.0],
              [1.0, 0.0, 0.0],
              [0.0, 0.0, 1.0],
              [0.0, 0.0, 1.0],
              [0.0, 0.0, 1.0],
              [0.0, 0.0, 1.0],
              [0.0, 0.0, 1.0],
              [1.0, 0.0, 0.0],
              [0.0, 0.0, 1.0],
              [0.0, 0.0, 1.0],
          ]
      )
      tensor_gt_label = tf.constant(gt_label, dtype=tf.float32)
      tensor_preds = tf.constant(preds, dtype=tf.float32)
      # Initialize
      mcc = MatthewsCorrelationCoefficient(3)
      # Update
      mcc.update_state(tensor_gt_label, tensor_preds)
      # Check results by comparing to results of scikit-learn matthew implementation.
      sklearn_result = sklearn_matthew(
          gt_label.argmax(axis=1), preds.argmax(axis=1)
      )
      check_results(mcc, sklearn_result)
  
file_path: addons/tensorflow_addons/metrics/tests/matthews_correlation_coefficient_test.py
imports: import tensorflow
  import numpy
  from tensorflow_addons.metrics import MatthewsCorrelationCoefficient
  from sklearn.metrics import matthews_corrcoef
label_desc: Use image node_id 4 for calling a global function with example usage: test_multiple_classes() without return types