code: def forward(self, state: rlt.FeatureData, action: rlt.FeatureData):
      (
          mus,
          sigmas,
          logpi,
          rewards,
          not_terminals,
          all_steps_hidden,
          last_step_hidden_and_cell,
      ) = self.mdnrnn(action.float_features, state.float_features)
      return rlt.MemoryNetworkOutput(
          mus=mus,
          sigmas=sigmas,
          logpi=logpi,
          reward=rewards,
          not_terminal=not_terminals,
          last_step_lstm_hidden=last_step_hidden_and_cell[0],
          last_step_lstm_cell=last_step_hidden_and_cell[1],
          all_steps_lstm_hidden=all_steps_hidden,
      )
  
file_path: ReAgent/reagent/models/world_model.py
imports: import torch
  from reagent.core import types
  from reagent.models.base import ModelBase
  from reagent.models.mdn_rnn import MDNRNN
label_desc: Use image node_id 3 for calling the MemoryNetwork obj's underlying member method code with example usage: obj.forward(state, action) and returns: rlt