def set_checkpoint(model, opt, progress_saver, savepath):
    """
    Store relevant parameters (model and progress saver, as well as parameter-namespace).
    Can be easily extend for other stuff.

    Args:
        model:          PyTorch network, network whose parameters are to be saved.
        opt:            argparse.Namespace, includes all training-specific parameters
        progress_saver: subclass of LOGGER-class, contains a running memory of all training metrics.
        savepath:       str, where to save checkpoint.
    Returns:
        Nothing!
    """
    torch.save(
        {
            "state_dict": model.state_dict(),
            "opt": opt,
            "progress": progress_saver,
        },
        savepath,
    )

    Store relevant parameters (model and progress saver, as well as parameter-namespace).
    Can be easily extend for other stuff.

    Args:
        model:          PyTorch network, network whose parameters are to be saved.
        opt:            argparse.Namespace, includes all training-specific parameters
        progress_saver: subclass of LOGGER-class, contains a running memory of all training metrics.
        savepath:       str, where to save checkpoint.
    Returns:
        Nothing!
    
examples/pytorch/hilander/PSS/Smooth_AP/src/auxiliaries.py