code: def lazy_init_no_master_weights(self):
      stash = self._amp_stash
      stash.all_fp16_params = []
      stash.all_fp32_params = []
      for i, param_group in enumerate(self.param_groups):
          for i, param in enumerate(param_group["params"]):
              if param.type() == "torch.cuda.HalfTensor":
                  stash.all_fp16_params.append(param)
              elif param.type() == "torch.cuda.FloatTensor":
                  stash.all_fp32_params.append(param)
              else:
                  raise TypeError(
                      "Optimizer's parameters must be either "
                      "torch.cuda.FloatTensor or torch.cuda.HalfTensor. "
                      "Received {}".format(param.type())
                  )
  
      stash.all_fp16_grad_stash = [None for _ in stash.all_fp16_params]
      stash.all_fp32_grad_stash = [None for _ in stash.all_fp32_params]
  
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 6 for calling a global function with example usage: lazy_init_no_master_weights() without return types