code: def begin_training(self, _=tuple(), pipeline=None, sgd=None, **kwargs):
          
          if self.model is True:
              self.model = self.Model(pipeline[0].model.nO)
              link_vectors_to_models(self.vocab)
          if sgd is None:
              sgd = self.create_optimizer()
          return sgd
docs: Allocate model, using width from tensorizer in pipeline.
  
          gold_tuples (iterable): Gold-standard training data.
          pipeline (list): The pipeline the model is part of.