code: def load(prefix, epoch, load_optimizer_states=False, **kwargs):
          
          sym, args, auxs = load_checkpoint(prefix, epoch)
          mod = Module(symbol=sym, **kwargs)
          mod._arg_params = args
          mod._aux_params = auxs
          mod.params_initialized = True
          if load_optimizer_states:
              mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
          return mod
docs: Creates a model from previously saved checkpoint.
  
          Parameters
          ----------
          prefix : str
              path prefix of saved model files. You should have
              "prefix-symbol.json", "prefix-xxxx.params", and
              optionally "prefix-xxxx.states", where xxxx is the
              epoch number.
          epoch : int
              epoch to load.
          load_optimizer_states : bool
              whether to load optimizer states. Checkpoint needs
              to have been made with save_optimizer_states=True.
          data_names : list of str
              Default is `('data')` for a typical model used in image classification.
          label_names : list of str
              Default is `('softmax_label')` for a typical model used in image
              classification.
          logger : Logger
              Default is `logging`.
          context : Context or list of Context
              Default is ``cpu()``.
          work_load_list : list of number
              Default ``None``, indicating uniform workload.
          fixed_param_names: list of str
              Default ``None``, indicating no network parameters are fixed.