code: def _generate_inner_optim(self, memory_efficient):
      param_groups = []
      tensors = []
      rand_in = 4
      for _iter in range(rand_in):
          a = torch.randn(rand_in, rand_in, requires_grad=True).half()
          b = torch.nn.Parameter(a)
          tensors.append(b)
      group = {"params": tensors}
      param_groups.append(group)
      self.optim = torch.optim.Adam(
          param_groups, lr=0.01, weight_decay=0.00001
      )
      self.fake_model = torch.randn(
          rand_in, rand_in, requires_grad=True
      )
      self.fake_model, self.fp16_optim = fp16_optimizer.initialize(
          self.fake_model,
          self.optim,
          "fake_opt",
          init_scale=2**6,
          memory_efficient=memory_efficient,
      )
  
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 1 for calling the TestFp16Optimizer obj's underlying member method code with example usage: obj._generate_inner_optim(memory_efficient) without return types