code: def prepare_backward_with_master_weights_FusedSGD(self):
      if self.materialize_master_grads:
          prepare_backward_with_master_weights(self)
      else:
          stash = self._amp_stash
  
          self._amp_lazy_init()
  
          for i, param in enumerate(stash.all_fp16_params):
              stash.all_fp16_grad_stash[i] = param.grad
              # Set up to leverage grad copy elision:
              param.grad = None
  
          for i, param in enumerate(stash.all_fp32_from_fp32_params):
              stash.all_fp32_from_fp32_grad_stash[i] = param.grad
              # Set up to leverage grad copy elision:
              param.grad = None
  
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 9 for calling a global function with example usage: prepare_backward_with_master_weights_FusedSGD() without return types