code: def test_mc_5_classes():
      actuals = tf.constant(
          [
              [1, 0, 0, 0, 0],
              [0, 0, 0, 1, 0],
              [0, 0, 0, 0, 1],
              [0, 1, 0, 0, 0],
              [0, 0, 1, 0, 0],
              [0, 0, 1, 0, 0],
              [1, 0, 0, 0, 0],
              [0, 1, 0, 0, 0],
          ],
          dtype=tf.float32,
      )
  
      predictions = tf.constant(
          [
              [0.85, 0, 0.15, 0, 0],
              [0, 0, 0, 1, 0],
              [0, 1, 0, 0, 0],
              [0.05, 0.90, 0.04, 0, 0.01],
              [0.10, 0, 0.81, 0.09, 0],
              [0.10, 0.045, 0, 0.81, 0.045],
              [1, 0, 0, 0, 0],
              [0, 0.85, 0, 0, 0.15],
          ],
          dtype=tf.float32,
      )
      # Initialize
      hl_obj = HammingLoss("multiclass", threshold=0.8)
      hl_obj.update_state(actuals, predictions)
      # Check results
      check_results(hl_obj, 0.25)
  
file_path: addons/tensorflow_addons/metrics/tests/hamming_test.py
imports: import numpy
  import tensorflow
  from tensorflow.keras import layers
  from tensorflow_addons.metrics import HammingLoss, hamming_distance
label_desc: Use image node_id 4 for calling a global function with example usage: test_mc_5_classes() without return types