code: def test_normalize(self):
      image = np.random.randint(0, 256, (224, 224, 3)) / 255
  
      # Number of mean values != number of channels
      with self.assertRaises(ValueError):
          normalize(image, mean=(0.5, 0.6), std=1)
  
      # Number of std values != number of channels
      with self.assertRaises(ValueError):
          normalize(image, mean=1, std=(0.5, 0.6))
  
      # Test result is correct - output data format is channels_first and normalization
      # correctly computed
      mean = (0.5, 0.6, 0.7)
      std = (0.1, 0.2, 0.3)
      expected_image = ((image - mean) / std).transpose((2, 0, 1))
  
      normalized_image = normalize(
          image, mean=mean, std=std, data_format="channels_first"
      )
      self.assertIsInstance(normalized_image, np.ndarray)
      self.assertEqual(normalized_image.shape, (3, 224, 224))
      self.assertTrue(np.allclose(normalized_image, expected_image))
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/tests/test_image_transforms.py
imports: import unittest
  import numpy
  from parameterized import parameterized
  from transformers.testing_utils import require_flax, require_tf, require_torch, require_vision
  from transformers.utils.import_utils import is_flax_available, is_tf_available, is_torch_available, is_vision_available
label_desc: Use image node_id 8 for calling the ImageTransformsTester obj's underlying member method code with example usage: obj.test_normalize() without return types