code: def test_call_numpy(self):
      # Initialize feature_extractor
      feature_extractor = self.feature_extraction_class(
          **self.feat_extract_dict
      )
      # create random numpy tensors
      image_inputs = prepare_image_inputs(
          self.feature_extract_tester,
          equal_resolution=False,
          numpify=True,
      )
      for image in image_inputs:
          self.assertIsInstance(image, np.ndarray)
  
      # Test not batched input
      encoded_images = feature_extractor(
          image_inputs[0], return_tensors="pt"
      ).pixel_values
  
      (
          expected_height,
          expected_width,
      ) = self.feature_extract_tester.get_expected_values(image_inputs)
  
      self.assertEqual(
          encoded_images.shape,
          (
              1,
              self.feature_extract_tester.num_channels,
              expected_height,
              expected_width,
          ),
      )
  
      # Test batched
      encoded_images = feature_extractor(
          image_inputs, return_tensors="pt"
      ).pixel_values
  
      (
          expected_height,
          expected_width,
      ) = self.feature_extract_tester.get_expected_values(
          image_inputs, batched=True
      )
  
      self.assertEqual(
          encoded_images.shape,
          (
              self.feature_extract_tester.batch_size,
              self.feature_extract_tester.num_channels,
              expected_height,
              expected_width,
          ),
      )
  
imports: import json
  import pathlib
  import unittest
  import numpy
  from transformers.testing_utils import require_torch, require_vision, slow
  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 6 for calling the YolosFeatureExtractionTest obj's underlying member method code with example usage: obj.test_call_numpy() without return types