code: def test_batch_standardize(all_test_images):
      # ImageDataGenerator.standardize should work on batches
      for test_images in all_test_images:
          img_list = []
          for im in test_images:
              img_list.append(utils.img_to_array(im)[None, ...])
  
          images = np.vstack(img_list)
          generator = image_data_generator.ImageDataGenerator(
              featurewise_center=True,
              samplewise_center=True,
              featurewise_std_normalization=True,
              samplewise_std_normalization=True,
              zca_whitening=True,
              rotation_range=90.0,
              width_shift_range=0.1,
              height_shift_range=0.1,
              shear_range=0.5,
              zoom_range=0.2,
              channel_shift_range=0.0,
              brightness_range=(1, 5),
              fill_mode="nearest",
              cval=0.5,
              horizontal_flip=True,
              vertical_flip=True,
          )
          generator.fit(images, augment=True)
  
          transformed = np.copy(images)
          for i, im in enumerate(transformed):
              transformed[i] = generator.random_transform(im)
          transformed = generator.standardize(transformed)
  
file_path: keras-preprocessing/tests/image/image_data_generator_test.py
imports: import numpy
  import pytest
  from PIL import Image
  from keras_preprocessing.image import image_data_generator, utils
label_desc: Use image node_id 9 for calling a global function with example usage: test_batch_standardize(all_test_images) without return types