code: def test_call_pil(self):
      # Initialize image_processor
      image_processor = self.image_processing_class(
          **self.image_processor_dict
      )
      # create random PIL images
      image_inputs = self.image_processor_tester.prepare_image_inputs(
          equal_resolution=False
      )
      for image in image_inputs:
          self.assertIsInstance(image, Image.Image)
  
      # Test not batched input
      expected_hidden_dim = (
          (
              self.image_processor_tester.patch_size["height"]
              * self.image_processor_tester.patch_size["width"]
          )
          * (self.image_processor_tester.num_channels - 1)
      ) + 2
  
      for max_patch in self.image_processor_tester.max_patches:
          # Test not batched input
          encoded_images = image_processor(
              image_inputs[0],
              return_tensors="pt",
              max_patches=max_patch,
          ).flattened_patches
          self.assertEqual(
              encoded_images.shape,
              (1, max_patch, expected_hidden_dim),
          )
  
          # Test batched
          encoded_images = image_processor(
              image_inputs, return_tensors="pt", max_patches=max_patch
          ).flattened_patches
          self.assertEqual(
              encoded_images.shape,
              (
                  self.image_processor_tester.batch_size,
                  max_patch,
                  expected_hidden_dim,
              ),
          )
  
imports: import unittest
  import numpy
  import requests
  from transformers.testing_utils import require_torch, require_vision
  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 4 for calling the Pix2StructImageProcessingTestFourChannels obj's underlying member method code with example usage: obj.test_call_pil() without return types