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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 _set_idx_to_vec_by_embeddings(self, token_embeddings, vocab_len, vocab_idx_to_token):
 """Sets the mapping between token indices and token embedding vectors. Parameters token_embeddings : instance or list `mxnet.contrib.text.embedding._TokenEmbedding` One or multiple pre-trained token embeddings to load. If it is a list of multiple embeddings, these embedding vectors will be concatenated for each token. vocab_len : int Length of vocabulary whose tokens are indexed in the token embedding. vocab_idx_to_token: list of str A list of indexed tokens in the vocabulary. These tokens are indexed in the token embedding. """


        new_vec_len = sum(embed.vec_len for embed in token_embeddings)
        new_idx_to_vec = nd.zeros(shape=(vocab_len, new_vec_len))

        col_start = 0
        # Concatenate all the embedding vectors in token_embeddings.
        for embed in token_embeddings:
            col_end = col_start + embed.vec_len
            # Cancatenate vectors of the unknown token.
            new_idx_to_vec[0, col_start:col_end] = embed.idx_to_vec[0]
            new_idx_to_vec[1:, col_start:col_end] = embed.get_vecs_by_tokens(vocab_idx_to_token[1:])
            col_start = col_end

        self._vec_len = new_vec_len
        self._idx_to_vec = new_idx_to_vec