I’m very new to pytorch and this might come as a stupid question; while going through the autograd 8 tutorial I came to this section where it’s written-

Now in this case  y  is no longer a scalar.  torch.autograd  could not compute the full Jacobian directly, but if we just want the vector-Jacobian product, simply pass the vector to  backward  as an argument:

v = torch.tensor([0.1, 1.0, 0.0001], dtype=torch.float)
y.backward(v)

print(x.grad)

Now, I don’t understand why are we using a different tensor v in order to compute gradients for x.