def metrics_to_examine(dataset, k_vals):
    """
    Please only use either of the following keys:
    -> Epochs, Time, Train Loss for training
    -> Epochs, Time, NMI, F1 & Recall @ k for validation

    Args:
        dataset: str, dataset for which a storing structure for LOGGER.progress_saver is to be made.
        k_vals:  list of int, Recall @ k - values.
    Returns:
        metric_dict: Dictionary representing the storing structure for LOGGER.progress_saver. See LOGGER.__init__() for an example.
    """
    metric_dict = {"train": ["Epochs", "Time", "Train Loss"]}

    if dataset == "vehicle_id":
        metric_dict["val"] = ["Epochs", "Time"]
        # Vehicle_ID uses three test sets
        for i in range(3):
            metric_dict["val"] += [
                "Set {} NMI".format(i),
                "Set {} F1".format(i),
            ]
            for k in k_vals:
                metric_dict["val"] += ["Set {} Recall @ {}".format(i, k)]
    else:
        metric_dict["val"] = ["Epochs", "Time", "NMI", "F1"]
        metric_dict["val"] += ["Recall @ {}".format(k) for k in k_vals]

    return metric_dict

    Please only use either of the following keys:
    -> Epochs, Time, Train Loss for training
    -> Epochs, Time, NMI, F1 & Recall @ k for validation

    Args:
        dataset: str, dataset for which a storing structure for LOGGER.progress_saver is to be made.
        k_vals:  list of int, Recall @ k - values.
    Returns:
        metric_dict: Dictionary representing the storing structure for LOGGER.progress_saver. See LOGGER.__init__() for an example.
    
examples/pytorch/hilander/PSS/Smooth_AP/src/auxiliaries.py