class_docstr: A world model used for simulation. Underlying is an RNN with fixed
  parameters. Given a sequence of actions and states, it outputs the next
  state's mixture means and reward.
code: def init_hidden(self, batch_size: int = 1) -> None:
      # (num_layers * num_directions, batch, hidden_size)
      self.hidden = (
          torch.zeros(
              self.lstm_num_hidden_layers,
              batch_size,
              self.lstm_num_hiddens,
          ),
          torch.zeros(
              self.lstm_num_hidden_layers,
              batch_size,
              self.lstm_num_hiddens,
          ),
      )
  
file_path: ReAgent/reagent/test/world_model/simulated_world_model.py
imports: import torch
  import torch.nn
label_desc: Use image node_id 3 for calling the SimulatedWorldModel obj's underlying member method code with example usage: obj.init_hidden(batch_size) without return types