code: def master_params_to_model_params(
      model_params, master_params, flat_master=False
  ):
      """
      Copy master parameters to model parameters.
  
      Args:
          model_params:  List of model parameters created by :func:`prep_param_lists`.
          master_params:  List of FP32 master parameters created by :func:`prep_param_lists`.  If ``master_params`` was created with ``flat_master=True``, ``flat_master=True`` should also be supplied to :func:`master_params_to_model_params`.
      """
      if flat_master:
          for model, master in zip(
              model_params,
              _unflatten_dense_tensors(
                  master_params[0].data, model_params
              ),
          ):
              model.data.copy_(master)
      else:
          for model, master in zip(model_params, master_params):
              model.data.copy_(master.data)
  
imports: import torch
  import torch.nn
  from torch.autograd import Variable
  from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
label_desc: Use image node_id 8 for calling a global function with example usage: master_params_to_model_params(model_params, master_params, flat_master) without return types