number of events. “seq length” is never explicitly specified as RNNs work in “one step at a time” mode. you can even batch sequences of varying lengths with nn.utils.rnn.pack_padded_sequence. In general, data loader for RNNs is a bit tricky (as you may want to sample timeseries and/or subsequences, depending on task).
1b. you don’t pass states across iterations, as new mini-batch implies drawing a new set of samples (timeseries or random start points)
same as with other NNs, weights are not sample specific, but transform some data across batch dimension the same way. Sharing across timesteps additionally implies that you can make timesteps with new events for as long as you need.