      The main difference this data loader tries to read more exmaples each
      time. But the limits are 1) all examples in dataset have the same shape, 2)
      data transfomer needs to process multiple examples at each time
      """
      def __init__(self, dataset, batch_size, shuffle):
          self.dataset = dataset
          self.batch_size = batch_size
          self.shuffle = shuffle
  
      def __iter__(self):
 
</s> remove     mnist_train = gluon.data.vision.FashionMNIST(root=root, train=True, transform=transform_mnist)
    mnist_test = gluon.data.vision.FashionMNIST(root=root, train=False, transform=transform_mnist)
    train_data = DataLoader(mnist_train, batch_size, shuffle=True)
    test_data = DataLoader(mnist_test, batch_size, shuffle=False)
 </s> add     
    mnist_train = gluon.data.vision.FashionMNIST(root=root, train=True, transform=None)
    mnist_test = gluon.data.vision.FashionMNIST(root=root, train=False, transform=None)
    train_data = DataLoader(mnist_train, batch_size, shuffle=True, transform = transform_mnist)
    test_data = DataLoader(mnist_test, batch_size, shuffle=False, transform = transform_mnist) </s> remove             yield (X[i*self.batch_size:(i+1)*self.batch_size],
                   y[i*self.batch_size:(i+1)*self.batch_size])
 </s> add             if self.transform is not None:
                yield self.transform(X[i*self.batch_size:(i+1)*self.batch_size], 
                                     y[i*self.batch_size:(i+1)*self.batch_size])
            else:
                yield (X[i*self.batch_size:(i+1)*self.batch_size],
                       y[i*self.batch_size:(i+1)*self.batch_size])
    def __init__(self, dataset, batch_size, shuffle, transform):
      The main difference this data loader tries to read more exmaples each
      time. But the limits are 1) all examples in dataset have the same shape, 2)
      data transfomer needs to process multiple examples at each time
      """
    def __init__(self, dataset, batch_size, shuffle, transform):
          self.dataset = dataset
          self.batch_size = batch_size
          self.shuffle = shuffle
  
      def __iter__(self):
keep keep keep keep replace keep keep keep keep keep
 fix for resize to 224 when training AlextNet in alexnet-gluon.md 
utils.py