Hi,
In this tutorial 39,  it is mentioned that backward receives a tensor (grad_output) but when we run loss.backward(), loss is a scalar. I see scalar is also a tensor but if grad_output is something other than the loss then my question is how I can  have access to the grad_output that is computed based on loss then?
@staticmethod
    def backward(ctx, grad_output):
        """
        In the backward pass we receive a Tensor containing the gradient of the loss
        with respect to the output, and we need to compute the gradient of the loss
        with respect to the input.
        """
        input, = ctx.saved_tensors
        grad_input = grad_output.clone()
        grad_input[input < 0] = 0
        return grad_input