code: def test_call_pil_four_channels(self):
      # Initialize feature_extractor
      feature_extractor = self.feature_extraction_class(
          **self.feat_extract_dict
      )
      # create random PIL images
      image_inputs = self.feature_extract_tester.prepare_inputs(
          equal_resolution=False
      )
      for image in image_inputs:
          self.assertIsInstance(image, Image.Image)
  
      # Test not batched input
      encoded_images = feature_extractor(
          image_inputs[0], return_tensors="pt"
      ).pixel_values
      self.assertEqual(
          encoded_images.shape,
          (
              1,
              self.expected_encoded_image_num_channels,
              self.feature_extract_tester.crop_size,
              self.feature_extract_tester.crop_size,
          ),
      )
  
      # Test batched
      encoded_images = feature_extractor(
          image_inputs, return_tensors="pt"
      ).pixel_values
      self.assertEqual(
          encoded_images.shape,
          (
              self.feature_extract_tester.batch_size,
              self.expected_encoded_image_num_channels,
              self.feature_extract_tester.crop_size,
              self.feature_extract_tester.crop_size,
          ),
      )
  
imports: import unittest
  import numpy
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
label_desc: Use image node_id 5 for calling the CLIPFeatureExtractionTestFourChannels obj's underlying member method code with example usage: obj.test_call_pil_four_channels() without return types