def METHOD_NAME(self, net, input_features, output_blob, s):
    """
    Applies Heaviside step function and Relu / exponentiation to features
    depending on the value of s.
    Inputs:
        net -- net with operators
        input_features -- features to processes
        output_blob -- output blob reference
        s -- degree to raise the transformed features
    """
    if s == 0:
        softsign_features = net.Softsign([input_features],
                                         net.NextScopedBlob('softsign'))
        return net.Relu(softsign_features, output_blob)
    elif s == 1:
        return net.Relu([input_features],
                        output_blob)
    else:
        relu_features = net.Relu([input_features],
                                 net.NextScopedBlob('relu_rand'))
        pow_features = net.Pow([input_features],
                               net.NextScopedBlob('pow_rand'),
                               exponent=float(s - 1))
        return net.Mul([relu_features, pow_features],
                       output_blob)