Explanation of JointDistributionSequential function


After reading the Tensorflow documentation for the JointDistributionSequential function, I still can’t understand how it works in the final inference.

Could someone explain to me, from a mathematical point of view, how the inference to the output distribution is possible.

For example, in the example below,

min=tfd.Normal(loc=1., scale=3.), #min
mode=tfd.LogNormal(loc=2., scale=4.), #mode
max=tfd.PERT(67., 69., 80.), #max
team_size=lambda max, mode, min : tfd.PERT(min, mode, max)

how is the inference to the Pert distribution possible ? By what principle is it possible to do this?
Indeed, we start from 3 different distributions and we infer towards the Pert distribution with these distributions as parameters.

Thanks in advance