code: def load_batch(self, batch_size=1, is_testing=False):
      data_type = "train" if not is_testing else "val"
      path_A = glob("./data/%s/%sA/*" % (self.dataset_name, data_type))
      path_B = glob("./data/%s/%sB/*" % (self.dataset_name, data_type))
  
      self.n_batches = int(min(len(path_A), len(path_B)) / batch_size)
      total_samples = self.n_batches * batch_size
  
      # Sample n_batches * batch_size from each path list so that model sees all
      # samples from both domains
      path_A = np.random.choice(path_A, total_samples, replace=False)
      path_B = np.random.choice(path_B, total_samples, replace=False)
  
      for i in range(self.n_batches - 1):
          batch_A = path_A[i * batch_size : (i + 1) * batch_size]
          batch_B = path_B[i * batch_size : (i + 1) * batch_size]
          imgs_A, imgs_B = [], []
          for img_A, img_B in zip(batch_A, batch_B):
              img_A = self.imread(img_A)
              img_B = self.imread(img_B)
  
              img_A = scipy.misc.imresize(img_A, self.img_res)
              img_B = scipy.misc.imresize(img_B, self.img_res)
  
              if not is_testing and np.random.random() > 0.5:
                  img_A = np.fliplr(img_A)
                  img_B = np.fliplr(img_B)
  
              imgs_A.append(img_A)
              imgs_B.append(img_B)
  
          imgs_A = np.array(imgs_A) / 127.5 - 1.0
          imgs_B = np.array(imgs_B) / 127.5 - 1.0
  
          yield imgs_A, imgs_B
  
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 3 for calling the DataLoader obj's underlying member method code with example usage: obj.load_batch(batch_size, is_testing) without return types