code: def resume_training(self, sgd=None, **cfg):
          
          if cfg.get("device", -1) >= 0:
              util.use_gpu(cfg["device"])
              if self.vocab.vectors.data.shape[1] >= 1:
                  self.vocab.vectors.data = Model.ops.asarray(self.vocab.vectors.data)
          link_vectors_to_models(self.vocab)
          if self.vocab.vectors.data.shape[1]:
              cfg["pretrained_vectors"] = self.vocab.vectors.name
          if sgd is None:
              sgd = create_default_optimizer(Model.ops)
          self._optimizer = sgd
          for name, proc in self.pipeline:
              if hasattr(proc, "_rehearsal_model"):
                  proc._rehearsal_model = deepcopy(proc.model)
          return self._optimizer
docs: Continue training a pre-trained model.
  
          Create and return an optimizer, and initialize "rehearsal" for any pipeline
          component that has a .rehearse() method. Rehearsal is used to prevent
          models from "forgetting" their initialised "knowledge". To perform
          rehearsal, collect samples of text you want the models to retain performance
          on, and call nlp.rehearse() with a batch of Doc objects.