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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 get_pret_embs(self, word_dims=None):
 """Read pre-trained embedding file Parameters word_dims : int or None vector size. Use `None` for auto-infer Returns ------- numpy.ndarray T x C numpy NDArray """

        assert (self._pret_embeddings is not None), "No pretrained file provided."
        pret_embeddings = gluonnlp.embedding.create(self._pret_embeddings[0], source=self._pret_embeddings[1])
        embs = [None] * len(self._id2word)
        for idx, vec in enumerate(pret_embeddings.idx_to_vec):
            embs[idx] = vec.asnumpy()
        if word_dims is None:
            word_dims = len(pret_embeddings.idx_to_vec[0])
        for idx, emb in enumerate(embs):
            if emb is None:
                embs[idx] = np.zeros(word_dims)
        pret_embs = np.array(embs, dtype=np.float32)
        return pret_embs / np.std(pret_embs)