code: def _update_fetched_model_params(models, model_params):
      for node in range(len(model_params)):
          for expert in range(len(model_params[node])):
              old_params = tuple(models[node][expert].parameters())
              for old, new in zip(
                  old_params,
                  _unflatten_dense_tensors(
                      model_params[node][expert], old_params
                  ),
              ):
                  old.copy_(new)
  
imports: import torch
  from torch.autograd import Function
  import fmoe_cuda
  from .utils import get_torch_default_comm
  from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
  from copy import deepcopy
label_desc: Use image node_id 7 for calling a global function with example usage: _update_fetched_model_params(models, model_params) without return types