It’s quite unclear how recurrent models function with the new trajectory view API.
def forward_rnn( self, inputs: TensorType, state: List[TensorType], seq_lens: TensorType ): print('got state', state) ... state = [torch.tensor([1,2,3])] return logits, state def get_initial_state(self): return 
got state  got state  got state  got state  ...
I want to build an accumulating representation that scales with the number of episodic timesteps, so various entries in a batch could have different state shapes.
Would anyone be able to provide some guidance on how to do this, or any insight on the interplay between
If my model inherits from
TorchModelV2 instead of
RecurrentNetwork, can I still propagate state?