  from mxnet.gluon import nn
  import mxnet as mx
  import numpy as np
  from time import time
  import matplotlib.pyplot as plt
  import matplotlib as mpl
  import random
  
  class DataLoader(object):
      """similiar to gluon.data.DataLoader, but might be faster.
 
</s> remove import random
 </s> add import matplotlib.pyplot as plt
import mxnet as mx
from mxnet import autograd, gluon, image, nd
from mxnet.gluon import nn, data as gdata, loss as gloss, utils as gutils
import numpy as np
 </s> remove from math import exp
from mxnet import gluon
from mxnet import autograd
from mxnet import nd
from mxnet import image
from mxnet.gluon import nn
import mxnet as mx
import numpy as np
 </s> add from math import exp                                                                                                         
import random </s> remove   - gluonbook
 </s> add  </s> remove                 y_vals.append(square_loss(net(X), y).mean().asnumpy())
 </s> add                 ls.append(loss(net(features), labels).mean().asnumpy())
    # To print more conveniently, use numpy. </s> remove                 output = net(features)
                loss = square_loss(output, label)
            loss.backward()
 </s> add                 l = loss(net(X), y)
            l.backward()
  from mxnet.gluon import nn
  import mxnet as mx
  import numpy as np
  from time import time

  import matplotlib as mpl
  import random
  
  class DataLoader(object):
      """similiar to gluon.data.DataLoader, but might be faster.
keep keep keep keep replace keep keep keep keep keep
 apply gluonbook in dl basics, cnn, remove gluonbook deps 
gluonbook/utils.py