def rnn_cell_forward(xt, a_prev, parameters):

  a_next = np.tanh(np.dot(Waa,a_prev)+np.dot(Wax,xt)+ba)
  yt_pred = softmax(np.dot(Wya,a_next)+by)   

  cache = (a_next, a_prev, xt, parameters)
  return a_next, yt_pred, cache

These are the forward pass equations I’m most familiar with. At the very first time step, the initial hidden state a_prev (h[0] initialized to 0) is never used to make output predictions. Whereas in the above PyTorch tutorial, a_prev gets to make the first output prediction. Are these changes in architecture/implementation? sorry, I’m still sort of confused.