code: def _test_logic_prepare(self):
      N, D_in, H, D_out = 64, 1000, 100, 10
      x1 = Variable(torch.randn(N, D_in), requires_grad=False)
      x2 = Variable(torch.randn(N, D_in), requires_grad=False)
      x3 = Variable(torch.randn(N, D_in), requires_grad=False)
      y1 = Variable(torch.randn(N, D_out), requires_grad=False)
      y2 = Variable(torch.randn(N, D_out), requires_grad=False)
      y3 = Variable(torch.randn(N, D_out), requires_grad=False)
      self.data_set = [[x1, y1], [x2, y2], [x3, y3]]
  
      w1 = Variable(torch.randn(D_in, H), requires_grad=True)
      w1_data = copy.deepcopy(w1.data)
      w1_copy = Variable(w1_data, requires_grad=True)
      ww1_data = copy.deepcopy(w1.data)
      ww1 = Variable(ww1_data, requires_grad=True)
      fake_w1_data = copy.deepcopy(w1.data)
  
      w2 = Variable(torch.randn(H, D_out), requires_grad=True)
      w2_data = copy.deepcopy(w2.data)
      w2_copy = Variable(w2_data, requires_grad=True)
      ww2_data = copy.deepcopy(w2.data)
      ww2 = Variable(ww2_data, requires_grad=True)
      fake_w2_data = copy.deepcopy(w2.data)
  
      self.parameters = [w1, w2]
      self.parameters_copy = [w1_copy, w2_copy]
      learning_rate = 0.001
      self.optimizer = torch.optim.Adam(
          self.parameters, lr=learning_rate
      )
      # for updating grads
      self.inner_optimizer = torch.optim.Adam(
          self.parameters_copy, lr=learning_rate
      )
      # for updating weights
      rand_in = 4
      fake_parameters = [fake_w1_data, fake_w2_data]
      fake_optimizer = torch.optim.Adam(
          fake_parameters, lr=learning_rate
      )
      self.fake_model = torch.randn(
          rand_in, rand_in, requires_grad=True
      )
      self.fake_model, self.fake_fp16_optim = fp16_optimizer.initialize(
          self.fake_model,
          fake_optimizer,
          "fake_opt",
          init_scale=2**10,
      )
  
      self.pparameters = [ww1, ww2]
      self.ooptimizer = torch.optim.Adam(
          self.pparameters, lr=learning_rate
      )
      self.fake_model = torch.randn(
          rand_in, rand_in, requires_grad=True
      )
      self.fake_model, self.ffp16_optim = fp16_optimizer.initialize(
          self.fake_model,
          self.ooptimizer,
          "fake_opt",
          init_scale=2**10,
      )
  
      # for memory efficient logic test preparation
      self.fake_model, self.MEoptim = fp16_optimizer.initialize(
          self.fake_model,
          self.optimizer,
          "fake_opt",
          init_scale=2**10,
          memory_efficient=True,
      )
      self.optimizer_c = self.inner_optimizer  # just change the name
      self.fake_model, self.MEoptim_c = fp16_optimizer.initialize(
          self.fake_model,
          self.optimizer_c,
          "fake_opt",
          init_scale=2**10,
          memory_efficient=True,
      )
  
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 7 for calling the TestFp16Optimizer obj's underlying member method code with example usage: obj._test_logic_prepare() without return types