code: def _master_params_to_model_params(self):
      if multi_tensor_applier.available:
          if len(self.all_fp16_params) > 0:
              multi_tensor_applier(
                  self.multi_tensor_scale,
                  self._dummy_overflow_buf,
                  [
                      self.all_fp32_from_fp16_params,
                      self.all_fp16_params,
                  ],
                  1.0,
              )
      else:
          for fp16_group, fp32_from_fp16_group in zip(
              self.fp16_groups, self.fp32_from_fp16_groups
          ):
              master_params_to_model_params(
                  fp16_group, fp32_from_fp16_group
              )
  
imports: import torch
  from torch import nn
  from torch.autograd import Variable
  from torch.nn.parameter import Parameter
  from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
  from ..amp._amp_state import _amp_state, maybe_print
  from ..amp.scaler import LossScaler
  from ..multi_tensor_apply import multi_tensor_applier
  from .fp16util import model_grads_to_master_grads, master_params_to_model_params, clip_grad_norm
label_desc: Use image node_id 6 for calling the FP16_Optimizer obj's underlying member method code with example usage: obj._master_params_to_model_params() without return types