def METHOD_NAME():
    parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
    parser.add_argument("--input_model_path", type=str, default="models/input_model.bin",
                        help=".")
    parser.add_argument("--output_model_path", type=str, default="models/output_model.bin",
                        help=".")
    parser.add_argument("--layers_num", type=int, default=12, help=".")
    args = parser.parse_args()
    input_model = torch.load(args.input_model_path, map_location='cpu')
    output_model = collections.OrderedDict()
    output_model["embeddings.word_embeddings.weight"] = \
        input_model["embedding.embedding_0.word.embedding.weight"]
    output_model["embeddings.position_embeddings.weight"] = \
        input_model["embedding.embedding_0.pos.embedding.weight"]
    output_model["embeddings.token_type_embeddings.weight"] = \
        input_model["embedding.embedding_0.seg.embedding.weight"][1:, :]
    output_model["embeddings.LayerNorm.weight"] = \
        input_model["embedding.embedding_0.layer_norm.gamma"]
    output_model["embeddings.LayerNorm.bias"] = \
        input_model["embedding.embedding_0.layer_norm.beta"]
    convert_sbert_transformer_encoder_from_uer_to_huggingface(input_model, output_model, args.layers_num)
    torch.save(output_model, args.output_model_path)