code: def _test_memory_efficient_logic_baseline(self):
      for _t in range(10):
          self.optimizer_c.zero_grad()
          for pair in self.data_set:
              y_pred_copy = (
                  pair[0]
                  .mm(self.parameters_copy[0])
                  .clamp(min=0)
                  .mm(self.parameters_copy[1])
              )
              loss_copy = (y_pred_copy - pair[1]).pow(
                  2
              ).sum() * self.MEoptim_c.loss_scaler.scale
              loss_copy = loss_copy / len(self.data_set)
              loss_copy.backward()
  
          self.MEoptim_c.loss_scaler.check_overflow(
              self.optimizer_c.param_groups
          )
          if not self.MEoptim_c.loss_scaler.is_overflow:
              for param in self.parameters_copy:
                  param.grad.data.div_(self.MEoptim_c.loss_scaler.scale)
              self.optimizer_c.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 14 for calling the TestFp16Optimizer obj's underlying member method code with example usage: obj._test_memory_efficient_logic_baseline() without return types