How to use the fairseq.utils.parse_embedding function in fairseq

    @classmethod
    def build_model(cls, args, src_dict, dst_dict):
        """Build a new model instance."""

        encoder_embed_dict = None
        if args.encoder_embed_path:
            encoder_embed_dict = utils.parse_embedding(args.encoder_embed_path)
            utils.print_embed_overlap(encoder_embed_dict, src_dict)

        decoder_embed_dict = None
        if args.decoder_embed_path:
            decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path)
            utils.print_embed_overlap(decoder_embed_dict, dst_dict)

        encoder = LSTMEncoder(
            src_dict,
            embed_dim=args.encoder_embed_dim,
            embed_dict=encoder_embed_dict,
            num_layers=args.encoder_layers,
            dropout_in=args.encoder_dropout_in,
            dropout_out=args.encoder_dropout_out,
        )
        decoder = LSTMDecoder(
            dst_dict,
            encoder_embed_dim=args.encoder_embed_dim,
            embed_dim=args.decoder_embed_dim,
            embed_dict=decoder_embed_dict,
            out_embed_dim=args.decoder_out_embed_dim,

def load_pretrained_embedding_from_file(embed_path, dictionary, embed_dim):
            num_embeddings = len(dictionary)
            padding_idx = dictionary.pad()
            embed_tokens = Embedding(num_embeddings, embed_dim, padding_idx)
            embed_dict = utils.parse_embedding(embed_path)
            utils.print_embed_overlap(embed_dict, dictionary)
            return utils.load_embedding(embed_dict, dictionary, embed_tokens)

def build_embedding(dictionary, embed_dim, path=None):
            num_embeddings = len(dictionary)
            padding_idx = dictionary.pad()
            emb = Embedding(num_embeddings, embed_dim, padding_idx)
            # if provided, load from preloaded dictionaries
            if path:
                embed_dict = utils.parse_embedding(path)
                utils.load_embedding(embed_dict, dictionary, emb)
            return emb

def build_embedding(dictionary, embed_dim, path=None):
            num_embeddings = len(dictionary)
            padding_idx = dictionary.pad()
            emb = Embedding(num_embeddings, embed_dim, padding_idx)
            # if provided, load from preloaded dictionaries
            if path:
                embed_dict = utils.parse_embedding(path)
                utils.load_embedding(embed_dict, dictionary, emb)
            return emb

def build_embedding(dictionary, embed_dim, path=None):
            num_embeddings = len(dictionary)
            padding_idx = dictionary.pad()
            emb = Embedding(num_embeddings, embed_dim, padding_idx)
            # if provided, load from preloaded dictionaries
            if path:
                embed_dict = utils.parse_embedding(path)
                utils.load_embedding(embed_dict, dictionary, emb)
            return emb

def build_embedding(dictionary, embed_dim, path=None):
            num_embeddings = len(dictionary)
            padding_idx = dictionary.pad()
            emb = Embedding(num_embeddings, embed_dim, padding_idx)
            # if provided, load from preloaded dictionaries
            if path:
                embed_dict = utils.parse_embedding(path)
                utils.load_embedding(embed_dict, dictionary, emb)
            return emb

def build_embedding(dictionary, embed_dim, path=None):
    num_embeddings = len(dictionary)
    padding_idx = dictionary.pad()
    emb = Embedding(num_embeddings, embed_dim, padding_idx)
    # if provided, load from preloaded dictionaries
    if path:
        embed_dict = utils.parse_embedding(path)
        utils.load_embedding(embed_dict, dictionary, emb)
    return emb

def build_embedding(dictionary, embed_dim, path=None):
            num_embeddings = len(dictionary)
            padding_idx = dictionary.pad()
            emb = Embedding(num_embeddings, embed_dim, padding_idx)
            # if provided, load from preloaded dictionaries
            if path:
                embed_dict = utils.parse_embedding(path)
                utils.load_embedding(embed_dict, dictionary, emb)
            return emb

def build_model(cls, args, task):
        """Build a new model instance."""
        # make sure that all args are properly defaulted (in case there are any new ones)
        base_architecture(args)

        encoder_embed_dict = None
        if args.encoder_embed_path:
            encoder_embed_dict = utils.parse_embedding(args.encoder_embed_path)
            utils.print_embed_overlap(encoder_embed_dict, task.source_dictionary)

        decoder_embed_dict = None
        if args.decoder_embed_path:
            decoder_embed_dict = utils.parse_embedding(args.decoder_embed_path)
            utils.print_embed_overlap(decoder_embed_dict, task.target_dictionary)

        encoder = FConvEncoder(
            dictionary=task.source_dictionary,
            embed_dim=args.encoder_embed_dim,
            embed_dict=encoder_embed_dict,
            convolutions=eval(args.encoder_layers),
            dropout=args.dropout,
            max_positions=args.max_source_positions,
        )
        decoder = FConvDecoder(
            dictionary=task.target_dictionary,
            embed_dim=args.decoder_embed_dim,
            embed_dict=decoder_embed_dict,
            convolutions=eval(args.decoder_layers),
            out_embed_dim=args.decoder_out_embed_dim,