code: def test_image_processor_from_and_save_pretrained(self):
      image_processor_first = self.image_processing_class(
          **self.image_processor_dict
      )
  
      with tempfile.TemporaryDirectory() as tmpdirname:
          image_processor_first.save_pretrained(tmpdirname)
          image_processor_second = (
              self.image_processing_class.from_pretrained(
                  tmpdirname
              ).to_dict()
          )
  
      image_processor_first = image_processor_first.to_dict()
      for key, value in image_processor_first.items():
          if key == "clusters":
              self.assertTrue(
                  np.array_equal(value, image_processor_second[key])
              )
          else:
              self.assertEqual(image_processor_first[key], value)
  
imports: import json
  import os
  import tempfile
  import unittest
  import numpy
  from datasets import load_dataset
  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 ImageProcessingTestMixin, prepare_image_inputs
label_desc: Use image node_id 7 for calling the ImageGPTImageProcessingTest obj's underlying member method code with example usage: obj.test_image_processor_from_and_save_pretrained() without return types