code: def test_resize_numpy(
      height,
      width,
      channels_first,
      label_type,
      interpolation,
      image_batch,
      art_warning,
  ):
      x, y = image_batch
  
      try:
          resize = ImageResize(
              height=height,
              width=width,
              channels_first=channels_first,
              label_type=label_type,
              interpolation=interpolation,
          )
  
          x_preprocess, y_preprocess = resize(x, y)
  
          if channels_first:
              assert x_preprocess.shape == (
                  x.shape[0],
                  x.shape[1],
                  height,
                  width,
              )
          else:
              assert x_preprocess.shape == (
                  x.shape[0],
                  height,
                  width,
                  x.shape[3],
              )
  
          if label_type == "classification":
              np.testing.assert_array_equal(y, y_preprocess)
          elif label_type == "object_detection":
              assert (
                  y[0]["boxes"].shape == y_preprocess[0]["boxes"].shape
              )
              np.testing.assert_array_equal(
                  y[0]["labels"], y_preprocess[0]["labels"]
              )
          else:
              assert y is None
  
      except ARTTestException as e:
          art_warning(e)
  
file_path: adversarial-robustness-toolbox/tests/preprocessing/image/test_image_resize.py
imports: import logging
  import numpy
  import cv2
  import pytest
  from art.config import ART_NUMPY_DTYPE
  from art.preprocessing.image import ImageResize, ImageResizePyTorch, ImageResizeTensorFlowV2
  from tests.utils import ARTTestException
label_desc: Use image node_id 2 for calling a global function with example usage: test_resize_numpy(height, width, channels_first, label_type, interpolation, image_batch, art_warning) without return types