code: def test_call_pil(self):
      # Initialize feature_extractor
      feature_extractor = self.feature_extraction_class(
          **self.feat_extract_dict
      )
      # create random PIL images
      image_inputs = prepare_image_inputs(
          self.feature_extract_tester, equal_resolution=False
      )
      for image in image_inputs:
          self.assertIsInstance(image, Image.Image)
  
      # Test not batched input (GLPNFeatureExtractor doesn't support batching)
      encoded_images = feature_extractor(
          image_inputs[0], return_tensors="pt"
      ).pixel_values
      self.assertTrue(
          encoded_images.shape[-1]
          % self.feature_extract_tester.size_divisor
          == 0
      )
      self.assertTrue(
          encoded_images.shape[-2]
          % self.feature_extract_tester.size_divisor
          == 0
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/tests/models/glpn/test_feature_extraction_glpn.py
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, prepare_image_inputs
label_desc: Use image node_id 5 for calling the GLPNFeatureExtractionTest obj's underlying member method code with example usage: obj.test_call_pil() without return types