def METHOD_NAME(config_filename, command_line_options, args):
    """
    :param str config_filename:
    :param list[str] command_line_options:
    :param args: argparse.Namespace
    """
    global config, engine, dataset
    rnn.METHOD_NAME(
        config_filename=config_filename,
        command_line_options=command_line_options,
        config_updates={"log": None, "need_data": False},
        extra_greeting="RETURNN dump-forward starting up.",
    )
    config = rnn.config
    engine = rnn.engine
    dataset_str = args.dataset
    if dataset_str in {"train", "dev", "eval", "search_data"}:
        dataset_str = "config:%s" % dataset_str
    extra_dataset_kwargs = {}
    if args.reset_partition_epoch:
        print("NOTE: We are resetting partition epoch to %i." % (args.reset_partition_epoch,))
        extra_dataset_kwargs["partition_epoch"] = args.reset_partition_epoch
    if args.reset_seq_ordering:
        print("NOTE: We will use %r seq ordering." % (args.reset_seq_ordering,))
        extra_dataset_kwargs["seq_ordering"] = args.reset_seq_ordering
    if args.reset_epoch_wise_filter:
        extra_dataset_kwargs["epoch_wise_filter"] = eval(args.reset_epoch_wise_filter)
    dataset = init_dataset(dataset_str, extra_kwargs=extra_dataset_kwargs)
    if hasattr(dataset, "epoch_wise_filter") and args.reset_epoch_wise_filter is None:
        if dataset.epoch_wise_filter:
            print("NOTE: Resetting epoch_wise_filter to None.")
            dataset.epoch_wise_filter = None
    if args.reset_partition_epoch:
        assert dataset.partition_epoch == args.reset_partition_epoch
    if args.reset_seq_ordering:
        assert dataset.seq_ordering == args.reset_seq_ordering
    config.set("task", "eval")
    if args.load:
        config.set("load", args.load)
    epoch, model_epoch_filename = Engine.get_epoch_model(config)
    engine.pretrain = pretrain_from_config(config)
    engine.custom_get_net_dict = config.typed_value("get_network")
    net_dict = engine.get_net_dict_for_epoch(epoch)
    engine.make_tf_session()
    engine.network = TFNetwork(name="root")
    engine.network.construct_layer(net_dict, args.layer)
    print("Load model:", model_epoch_filename)
    engine.network.load_params_from_file(model_epoch_filename, session=engine.tf_session)