fitsemivariance.py: nugget if statement

[SimonMolinsky]

When we calculate theoretical semivariance then we must set a nugget for each model. As I understand from the book (and the code itself) a nugget value should be set to zero (?) even if our first lag distance from empirical semivariance is not 0.0. But I do not understand the else statement in a condition when we set a nugget. Why do we choose the second value of lag if the first is equal to 0? Then we will start with nugget different than 0. Is this a correct way of setting our nugget value?

Code related to the question:

if s[0][0] is not 0.0:      # c0, nugget
  c0 = 0.0
else:
  c0 = s[0][1]