code: def test_normalize_image(self):
      feature_extractor = ImageFeatureExtractionMixin()
      image = get_random_image(16, 32)
      array = np.array(image)
      mean = [0.1, 0.5, 0.9]
      std = [0.2, 0.4, 0.6]
  
      # PIL Image are converted to NumPy arrays for the normalization
      normalized_image = feature_extractor.normalize(image, mean, std)
      self.assertTrue(isinstance(normalized_image, np.ndarray))
      self.assertEqual(normalized_image.shape, (3, 16, 32))
  
      # During the conversion rescale and channel first will be applied.
      expected = array.transpose(2, 0, 1).astype(np.float32) * (
          1 / 255.0
      )
      np_mean = np.array(mean).astype(np.float32)[:, None, None]
      np_std = np.array(std).astype(np.float32)[:, None, None]
      expected = (expected - np_mean) / np_std
      self.assertTrue(np.array_equal(normalized_image, expected))
  
imports: import unittest
  import datasets
  import numpy
  import pytest
  from transformers import is_torch_available, is_vision_available
  from transformers.image_utils import ChannelDimension, get_channel_dimension_axis
  from transformers.testing_utils import require_torch, require_vision
label_desc: Use image node_id 10 for calling the ImageFeatureExtractionTester obj's underlying member method code with example usage: obj.test_normalize_image() without return types