code: def test_resize_max_size_respected(self):
      image_processor = self.image_processing_class(
          **self.image_processor_dict
      )
  
      # create torch tensors as image
      image = torch.randint(0, 256, (3, 100, 1500), dtype=torch.uint8)
      processed_image = image_processor(
          image,
          size={"longest_edge": 1333, "shortest_edge": 800},
          do_pad=False,
          return_tensors="pt",
      )["pixel_values"]
  
      self.assertTrue(processed_image.shape[-1] <= 1333)
      self.assertTrue(processed_image.shape[-2] <= 800)
  
imports: import json
  import pathlib
  import unittest
  from transformers.testing_utils import require_torch, require_vision, slow
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_image_processing_common import AnnotationFormatTestMixin, ImageProcessingTestMixin, prepare_image_inputs
label_desc: Use image node_id 6 for calling the YolosImageProcessingTest obj's underlying member method code with example usage: obj.test_resize_max_size_respected() without return types