code: def _master_params_to_model_params(self):
      stash = self._amp_stash
      if multi_tensor_applier.available:
          if len(stash.all_fp16_params) > 0:
              multi_tensor_applier(
                  stash.multi_tensor_scale,
                  stash.dummy_overflow_buf,
                  [
                      stash.all_fp32_from_fp16_params,
                      stash.all_fp16_params,
                  ],
                  1.0,
              )
      else:
          for fp16_group, fp32_from_fp16_group in zip(
              stash.fp16_groups, stash.fp32_from_fp16_groups
          ):
              master_params_to_model_params(
                  fp16_group, fp32_from_fp16_group
              )
  
imports: import types
  from ..fp16_utils import master_params_to_model_params
  from ..multi_tensor_apply import multi_tensor_applier
  from ._amp_state import maybe_print
  import torch
  from ..optimizers import FusedSGD
label_desc: Use image node_id 1 for calling a global function with example usage: _master_params_to_model_params() without return types