code: def test_call_pytorch(self):
      # Initialize image_processing
      image_processing = self.image_processing_class(
          **self.image_processor_dict
      )
      # create random PyTorch tensors
      image_inputs = self.image_processor_tester.prepare_image_inputs(
          equal_resolution=False, torchify=True
      )
      expected_output_image_shape = (
          self.image_processor_tester.expected_output_image_shape(
              image_inputs
          )
      )
  
      for image in image_inputs:
          self.assertIsInstance(image, torch.Tensor)
  
      # Test not batched input
      encoded_images = image_processing(
          image_inputs[0], return_tensors="pt"
      ).input_ids
      self.assertEqual(
          tuple(encoded_images.shape), (1, *expected_output_image_shape)
      )
  
      # Test batched
      encoded_images = image_processing(
          image_inputs, return_tensors="pt"
      ).input_ids
      self.assertEqual(
          tuple(encoded_images.shape),
          (
              self.image_processor_tester.batch_size,
              *expected_output_image_shape,
          ),
      )
  
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 12 for calling the ImageGPTImageProcessingTest obj's underlying member method code with example usage: obj.test_call_pytorch() without return types