Hello, I have a neural network which has a fully connected (FC) layer with relu activation function before that (i.e. the input of this FC layer comes from a relu activation) if we called this input as ‘x’ and this layer as ‘h’, I like to compute the Jacobean of h with respect to its input x. So, we expect a 2D matrix with partial derivative of dh_j/dx_i. I used ‘javobian’ it gave me a 4D matrix (x and h are a 2D matrices). On the other hand ‘vjp’ gave me a 2D matrix… I’m confused which one is true!!