Discretize normal distribution to get prob of a random variable

Suppose I draw randomly from a normal distribution with mean zero and standard deviation represented by a vector of, say, dimension 3 with

scale_rng=np.array([1,2,3])
eps=np.random.normal(0,scale_rng)

I need to compute a weighted average based on some simulations for which I draw the above mentioned eps. The weights of this average are "the probability of eps" (hence I will have a vector with 3 weights). Now, I know that I cannot think of this being the points on the pdf corresponding to the values randomly drawn because a normal random variable is continuous and as such the pdf at a certain point is zero. Hence, the only solution I Found out is to discretize a normal distribution with a certain number of bins and then find the probability that a value extracted with the code of above is actually drawn. How could I do this in Python?