You can just clip the weights of the parameters after each optimization update.
class WeightClipper(object):

    def __init__(self, frequency=5):
        self.frequency = frequency

    def __call__(self, module):
        # filter the variables to get the ones you want
        if hasattr(module, 'weight'):
            w = module.weight.data
            w = w.clamp(-1,1)


model = Net()
clipper = WeightClipper()
model.apply(clipper)

created with inspiration from this post 479