code: def test_call_pil(self):
      # Initialize processor
      processor = self.processing_class(**self.processor_dict)
      # create random PIL images
      image_inputs = self.processing_tester.prepare_image_inputs(
          equal_resolution=False
      )
      for image in image_inputs:
          self.assertIsInstance(image, Image.Image)
  
      # Test not batched input
      encoded_images = processor(
          image_inputs[0], ["semantic"], return_tensors="pt"
      ).pixel_values
  
      (
          expected_height,
          expected_width,
          expected_sequence_length,
      ) = self.processing_tester.get_expected_values(image_inputs)
  
      self.assertEqual(
          encoded_images.shape,
          (
              1,
              self.processing_tester.num_channels,
              expected_height,
              expected_width,
          ),
      )
  
      tokenized_task_inputs = processor(
          image_inputs[0], ["semantic"], return_tensors="pt"
      ).task_inputs
  
      self.assertEqual(
          tokenized_task_inputs.shape,
          (1, expected_sequence_length),
      )
  
      # Test batched
      (
          expected_height,
          expected_width,
          expected_sequence_length,
      ) = self.processing_tester.get_expected_values(
          image_inputs, batched=True
      )
  
      encoded_images = processor(
          image_inputs,
          ["semantic"] * len(image_inputs),
          return_tensors="pt",
      ).pixel_values
      self.assertEqual(
          encoded_images.shape,
          (
              self.processing_tester.batch_size,
              self.processing_tester.num_channels,
              expected_height,
              expected_width,
          ),
      )
  
      tokenized_task_inputs = processor(
          image_inputs,
          ["semantic"] * len(image_inputs),
          return_tensors="pt",
      ).task_inputs
  
      self.assertEqual(
          tokenized_task_inputs.shape,
          (self.processing_tester.batch_size, expected_sequence_length),
      )
  
imports: import json
  import os
  import tempfile
  import unittest
  import numpy
  from datasets import load_dataset
  from huggingface_hub import hf_hub_download
  from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_image_processing_common import prepare_image_inputs
label_desc: Use image node_id 5 for calling the OneFormerProcessingTest obj's underlying member method code with example usage: obj.test_call_pil() without return types