def METHOD_NAME(item, keep_features=True):
    requires_backends(METHOD_NAME, ["cython"])
    if keep_features and "edge_attr" in item.keys():  # edge_attr
        edge_attr = np.asarray(item["edge_attr"], dtype=np.int64)
    else:
        edge_attr = np.ones((len(item["edge_index"][0]), 1), dtype=np.int64)  # same embedding for all
    if keep_features and "node_feat" in item.keys():  # input_nodes
        node_feature = np.asarray(item["node_feat"], dtype=np.int64)
    else:
        node_feature = np.ones((item["num_nodes"], 1), dtype=np.int64)  # same embedding for all
    edge_index = np.asarray(item["edge_index"], dtype=np.int64)
    input_nodes = convert_to_single_emb(node_feature) + 1
    num_nodes = item["num_nodes"]
    if len(edge_attr.shape) == 1:
        edge_attr = edge_attr[:, None]
    attn_edge_type = np.zeros([num_nodes, num_nodes, edge_attr.shape[-1]], dtype=np.int64)
    attn_edge_type[edge_index[0], edge_index[1]] = convert_to_single_emb(edge_attr) + 1
    # node adj matrix [num_nodes, num_nodes] bool
    adj = np.zeros([num_nodes, num_nodes], dtype=bool)
    adj[edge_index[0], edge_index[1]] = True
    shortest_path_result, path = algos_graphormer.floyd_warshall(adj)
    max_dist = np.amax(shortest_path_result)
    input_edges = algos_graphormer.gen_edge_input(max_dist, path, attn_edge_type)
    attn_bias = np.zeros([num_nodes + 1, num_nodes + 1], dtype=np.single)  # with graph token
    # combine
    item["input_nodes"] = input_nodes + 1  # we shift all indices by one for padding
    item["attn_bias"] = attn_bias
    item["attn_edge_type"] = attn_edge_type
    item["spatial_pos"] = shortest_path_result.astype(np.int64) + 1  # we shift all indices by one for padding
    item["in_degree"] = np.sum(adj, axis=1).reshape(-1) + 1  # we shift all indices by one for padding
    item["out_degree"] = item["in_degree"]  # for undirected graph
    item["input_edges"] = input_edges + 1  # we shift all indices by one for padding
    if "labels" not in item:
        item["labels"] = item["y"]
    return item