code: def test_ml_5_classes():
      actuals = tf.constant(
          [
              [1, 0, 0, 0, 0],
              [0, 0, 1, 1, 0],
              [0, 1, 0, 1, 0],
              [0, 1, 1, 0, 0],
              [0, 0, 1, 1, 0],
              [0, 0, 1, 1, 0],
              [1, 0, 0, 0, 1],
              [0, 1, 1, 0, 0],
          ],
          dtype=tf.float32,
      )
      predictions = tf.constant(
          [
              [1, 0.75, 0.2, 0.55, 0],
              [0.65, 0.22, 0.97, 0.88, 0],
              [0, 1, 0, 1, 0],
              [0, 0.85, 0.9, 0.34, 0.5],
              [0.4, 0.65, 0.87, 0, 0.12],
              [0.66, 0.55, 1, 0.98, 0],
              [0.95, 0.34, 0.67, 0.65, 0.10],
              [0.45, 0.97, 0.89, 0.67, 0.46],
          ],
          dtype=tf.float32,
      )
      # Initialize
      hl_obj = HammingLoss("multilabel", threshold=0.7)
      hl_obj.update_state(actuals, predictions)
      # Check results
      check_results(hl_obj, 0.075)
  
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 6 for calling a global function with example usage: test_ml_5_classes() without return types