Hi @orena1,
First, in autograd terms, leaf nodes are the input of the forward (the last  on which backpropagation will be applied) and the root the output of the forward network.
Remember that when you use MSELoss, the last operation to be applied is a reduction (by default Mean but you can also choose sum with the reduction 1 arg ).
So the first backward operation to be computed will the mean, as you can see here:
>>> loss(y, t)
tensor(0.1328, grad_fn=<MeanBackward0>) # <==

an operation which will propagate the gradient to every output node contributing to this mean.
I hope everything is clear.