code: def load_data(self, domain, batch_size=1, is_testing=False):
      data_type = (
          "train%s" % domain if not is_testing else "test%s" % domain
      )
      path = glob("./data/%s/%s/*" % (self.dataset_name, data_type))
  
      batch_images = np.random.choice(path, size=batch_size)
  
      imgs = []
      for img_path in batch_images:
          img = self.imread(img_path)
          if not is_testing:
              img = scipy.misc.imresize(img, self.img_res)
  
              if np.random.random() > 0.5:
                  img = np.fliplr(img)
          else:
              img = scipy.misc.imresize(img, self.img_res)
          imgs.append(img)
  
      imgs = np.array(imgs) / 127.5 - 1.0
  
      return imgs
  
imports: import pickle
  import os
  from keras.datasets import mnist, cifar100, cifar10
  from keras.preprocessing.image import ImageDataGenerator, load_img, save_img, img_to_array
  import pandas
  import numpy
  from os import walk, getcwd
  import h5py
  import scipy
  from glob import glob
  from keras.applications import vgg19
  from keras import backend
  from keras.utils import to_categorical
  import pdb
label_desc: Use image node_id 2 for calling the DataLoader obj's underlying member method code with example usage: obj.load_data(domain, batch_size, is_testing) and returns: imgs