          self.batch_size = batch_size
          self.shuffle = shuffle
  
      def __iter__(self):
          data = self.dataset[:]
          X = data[0]
          y = nd.array(data[1])
          n = X.shape[0]
 
</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     def __init__(self, dataset, batch_size, shuffle):
 </s> add     def __init__(self, dataset, batch_size, shuffle, transform): </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])
        self.transform = transform
          self.batch_size = batch_size
          self.shuffle = shuffle
        self.transform = transform
  
      def __iter__(self):
          data = self.dataset[:]
          X = data[0]
          y = nd.array(data[1])
          n = X.shape[0]
keep add keep keep keep keep keep keep
 fix for resize to 224 when training AlextNet in alexnet-gluon.md 
utils.py