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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def begin_training(self, _=tuple(), pipeline=None, sgd=None, **kwargs):
 """Allocate model, using width from tensorizer in pipeline. gold_tuples (iterable):
 Gold-standard training data. pipeline (list):
 The pipeline the model is part of. """

        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