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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, get_gold_tuples=None, sgd=None, component_cfg=None, **cfg):
 """Allocate models, pre-process training data and acquire a trainer and optimizer. Used as a contextmanager. get_gold_tuples (function):
 Function returning gold data component_cfg (dict):
 Config parameters for specific components. **cfg: Config parameters. RETURNS: An optimizer. DOCS: https://spacy.io/api/language#begin_training """

        if get_gold_tuples is None:
            get_gold_tuples = lambda: []
        # Populate vocab
        else:
            for _, annots_brackets in get_gold_tuples():
                for annots, _ in annots_brackets:
                    for word in annots[1]:
                        _ = self.vocab[word]  # noqa: F841
        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
        if component_cfg is None:
            component_cfg = {}
        for name, proc in self.pipeline:
            if hasattr(proc, "begin_training"):
                kwargs = component_cfg.get(name, {})
                kwargs.update(cfg)
                proc.begin_training(
                    get_gold_tuples,
                    pipeline=self.pipeline,
                    sgd=self._optimizer,
                    **kwargs
                )
        return self._optimizer