def begin_training(self, _=tuple(), pipeline=None, sgd=None, **kwargs):
if self.model is True:<EOL><INDENT>self.model = self.Model(pipeline[<NUM_LIT:0>].model.nO)<EOL>link_vectors_to_models(self.vocab)<EOL><DEDENT>if sgd is None:<EOL><INDENT>sgd = self.create_optimizer()<EOL><DEDENT>return sgd<EOL>
Allocate model, using width from tensorizer in pipeline.

        gold_tuples (iterable): Gold-standard training data.
        pipeline (list): The pipeline the model is part of.