code: def test_multi_channels():
      channels = 3
      batch_size = 2048
      image = [
          [[0], [0], [0], [0], [0]],
          [[0], [1], [1], [1], [0]],
          [[0], [1], [1], [1], [0]],
          [[0], [1], [1], [1], [0]],
          [[0], [0], [0], [0], [0]],
      ]
      expected_output = np.tile(
          np.expand_dims(
              np.array(
                  [
                      0,
                      0,
                      0,
                      0,
                      0,
                      0,
                      1,
                      1,
                      1,
                      0,
                      0,
                      1,
                      2,
                      1,
                      0,
                      0,
                      1,
                      1,
                      1,
                      0,
                      0,
                      0,
                      0,
                      0,
                      0,
                  ]
              ),
              axis=-1,
          ),
          [batch_size, 3],
      )
      image = np.tile(image, [1, 1, channels])
      images = tf.constant([image] * batch_size, dtype=tf.uint8)
  
      output = dist_ops.euclidean_dist_transform(
          images, dtype=tf.float32
      )
      output_flat = tf.reshape(output, [-1, 3])
      assert output.shape == [batch_size, 5, 5, channels]
      test_utils.assert_allclose_according_to_type(
          output_flat, expected_output
      )
  
file_path: addons/tensorflow_addons/image/tests/distance_transform_test.py
imports: import pytest
  import numpy
  import tensorflow
  from tensorflow_addons.image import distance_transform
  from tensorflow_addons.utils import test_utils
label_desc: Use image node_id 6 for calling a global function with example usage: test_multi_channels() without return types