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_weight(self) -> None:
      torch.manual_seed(3212)
      for _, p in self.lstm.named_parameters():
          nn.init.normal_(p, 0, 1)
      for _, p in self.gmm_linear.named_parameters():
          nn.init.normal_(p, 0, 1)
  
file_path: ReAgent/reagent/test/world_model/simulated_world_model.py
imports: import torch
  import torch.nn
label_desc: Use image node_id 4 for calling the SimulatedWorldModel obj's underlying member method code with example usage: obj.init_weight() without return types