How does PyTorch handle gradient flow through branching networks?

Gradients from multiple branches are summed at convergence points in the computation graph.

When a tensor feeds multiple paths, autograd records multiple backward edges. During backpropagation, gradients from all branches are accumulated (summed) at shared nodes according to the chain rule.