code: def _test_master_maintain_logic_experiment(self):
      for _t in range(10):
          self.ffp16_optim.zero_grad()
          for pair in self.data_set:
              yy_pred = (
                  pair[0]
                  .mm(self.pparameters[0])
                  .clamp(min=0)
                  .mm(self.pparameters[1])
              )
              lloss = (yy_pred - pair[1]).pow(2).sum()
              lloss = lloss / len(self.data_set)
              with fp16_optimizer.scale_loss(
                  lloss, self.ffp16_optim, delay_unscale=False
              ) as scaled_loss:
                  scaled_loss.backward()
          # Run backprop
          # Check for overflow
          self.ffp16_optim.step()
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import copy
  import unittest
  import torch
  from pytext.optimizer import fp16_optimizer
  from torch.autograd import Variable
label_desc: Use image node_id 9 for calling the TestFp16Optimizer obj's underlying member method code with example usage: obj._test_master_maintain_logic_experiment() without return types