code: def image_batch(height, width, channels_first, label_type):
      channels = 3
  
      if channels_first:
          data_shape = (2, channels, height, width)
      else:
          data_shape = (2, height, width, channels)
      x = (0.5 * np.ones(data_shape)).astype(ART_NUMPY_DTYPE)
  
      if label_type == "classification":
          y = np.arange(len(x))
      elif label_type == "object_detection":
          y = []
          for _ in range(len(x)):
              y_1, x_1 = np.random.uniform(0, (height / 2, width / 2))
              y_2, x_2 = np.random.uniform(
                  (y_1 + 3, x_1 + 3), (height, width)
              )
              target_dict = {
                  "boxes": np.array([[x_1, y_1, x_2, y_2]]),
                  "labels": np.array([0]),
              }
              y.append(target_dict)
      else:
          y = None
  
      return x, y
  
file_path: adversarial-robustness-toolbox/tests/preprocessing/image/test_image_square_pad.py
imports: import logging
  import numpy
  import pytest
  from art.config import ART_NUMPY_DTYPE
  from art.preprocessing.image import ImageSquarePad, ImageSquarePadPyTorch, ImageSquarePadTensorFlowV2
  from tests.utils import ARTTestException
label_desc: Use image node_id 1 for calling a global function with example usage: image_batch(height, width, channels_first, label_type) and returns: x,  y