human
Solve the following python programming problem given the following code:
# Define a type to store the functional and gradient.
class fg_pair:
    def __init__(self, f, g):
        self.f = f
        self.g = g
    
    def __add__(self, other):
        f = self.f + other.f
        g = self.g + other.g
        
        return fg_pair(f, g)
    
    def __radd__(self, other):
        if other == 0:
            return self
        else:
            return self.__add__(other)
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

Dask specifics
Previously we defined a function to calculate the individual contribution to the functional and gradient for each shot, which was then used in a loop over all shots. However, when using distributed frameworks such as Dask we instead think in terms of creating a worklist which gets mapped onto the worker pool. The sum reduction is also performed in parallel. For now however we assume that the scipy.optimize.minimize itself is running on the master process; this is a reasonable simplification because the computational cost of calculating (f, g) far exceeds the other compute costs.
Because we want to be able to use standard reduction operators such as sum on (f, g) we first define it as a type so that we can define the __add__ (and __rand__ method).