@ptrblck, I have a doubt, might be silly to ask🤐.
But anyways, when we call loss.backward, will grad_input, grad_output, grad_weight and grad_bias become like these?
grad_input = gradient of loss w.r.t input,
grad_output = gradient of loss w.r.t output,
grad_weight = gradient of loss w.r.t weight,
grad_bias = gradient of loss w.r.t bias.
And what do grad_input, grad_output, grad_weight and grad_bias mean as per this tutorial 2? Gradients of what and w.r.t. what?