Asking for explanation in scale model parameters

I have seen a method to scale model, in PySyft, and I had some guesses, but I would appreciate it if you could please explain what is happening in here.
I mention my questions in the code as comments.

def scale_model(model, scale):
    """Scale the parameters of a model.
        model (torch.nn.Module): the models whose parameters will be scaled.
        scale (float): the scaling factor.
        torch.nn.Module: the module with scaled parameters.
    params = model.named_parameters()
    # in here, does params have references of model parameters? It only makes sense
    # for me only in this, way, but how a method return a pointer?
    dict_params = dict(params)
    # I was assuming that casting params as a dictionary, would change the pointer 
    # assignments, but it seems that it still has its references
    with torch.no_grad():
        for name, param in dict_params.items():
            dict_params[name].set_(dict_params[name].data * scale)
            # I am seeing that in the line above, the scale is applying to each layer separately. Is it 
            # correct? and then we have modified the parameters, and the data is changing in the 
            # model, as well
    return model