:meth: `merge_wgts` insert pretrained weights and vocab into a new set of weights and vocab;
    use average if vocab not in pretrained vocab
    :param int em_sz: embedding size
    :param wgts: torch model weights
    :param list itos_pre: pretrained list of vocab
    :param list itos_new: list of new vocab
    :return: merged torch model weights

def merge_wgts(em_sz, wgts, itos_pre, itos_new):
    """
    :meth: `merge_wgts` insert pretrained weights and vocab into a new set of weights and vocab;
    use average if vocab not in pretrained vocab
    :param int em_sz: embedding size
    :param wgts: torch model weights
    :param list itos_pre: pretrained list of vocab
    :param list itos_new: list of new vocab
    :return: merged torch model weights
    """
    vocab_size = len(itos_new)
    enc_wgts = wgts["0.encoder.weight"].numpy()

    # Average weight of encoding
    row_m = enc_wgts.mean(0)
    stoi_pre = collections.defaultdict(
        lambda: -1, {v: k for k, v in enumerate(itos_pre)}
    )

    # New embedding based on classification dataset
    new_w = np.zeros((vocab_size, em_sz), dtype=np.float32)

    for i, w in enumerate(itos_new):
        r = stoi_pre[w]
        # Use pretrianed embedding if present; else use the average
        new_w[i] = enc_wgts[r] if r >= 0 else row_m

    wgts["0.encoder.weight"] = torch.tensor(new_w)
    wgts["0.encoder_dp.emb.weight"] = torch.tensor(np.copy(new_w))
    wgts["1.decoder.weight"] = torch.tensor(np.copy(new_w))

    return wgts