code: def all_test_images():
      img_w = img_h = 20
      rgb_images = []
      rgba_images = []
      gray_images = []
      for n in range(8):
          bias = np.random.rand(img_w, img_h, 1) * 64
          variance = np.random.rand(img_w, img_h, 1) * (255 - 64)
          imarray = np.random.rand(img_w, img_h, 3) * variance + bias
          im = Image.fromarray(imarray.astype("uint8")).convert("RGB")
          rgb_images.append(im)
  
          imarray = np.random.rand(img_w, img_h, 4) * variance + bias
          im = Image.fromarray(imarray.astype("uint8")).convert("RGBA")
          rgba_images.append(im)
  
          imarray = np.random.rand(img_w, img_h, 1) * variance + bias
          im = Image.fromarray(
              imarray.astype("uint8").squeeze()
          ).convert("L")
          gray_images.append(im)
  
      return [rgb_images, rgba_images, gray_images]
  
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 1 for calling a global function with example usage: all_test_images() and returns: list