code: def make_model(self, env):
      hidden_size = 20
  
      obs_size = env.observation_space.low.size
  
      def weight_scale(layer, scale):
          with torch.no_grad():
              layer.weight.mul_(scale)
          return layer
  
      if self.recurrent:
          v = RecurrentSequential(
              nn.LSTM(
                  num_layers=1,
                  input_size=obs_size,
                  hidden_size=hidden_size,
              ),
              weight_scale(nn.Linear(hidden_size, 1), 1e-1),
          )
          if self.discrete:
              n_actions = env.action_space.n
              pi = RecurrentSequential(
                  NonCudnnLSTM(
                      num_layers=1,
                      input_size=obs_size,
                      hidden_size=hidden_size,
                  ),
                  weight_scale(nn.Linear(hidden_size, n_actions), 1e-1),
                  SoftmaxCategoricalHead(),
              )
          else:
              action_size = env.action_space.low.size
              pi = RecurrentSequential(
                  NonCudnnLSTM(
                      num_layers=1,
                      input_size=obs_size,
                      hidden_size=hidden_size,
                  ),
                  weight_scale(
                      nn.Linear(hidden_size, action_size), 1e-1
                  ),
                  GaussianHeadWithStateIndependentCovariance(
                      action_size=action_size,
                      var_type="diagonal",
                      var_func=lambda x: torch.exp(2 * x),
                      var_param_init=0,
                  ),
              )
      else:
          v = nn.Sequential(
              nn.Linear(obs_size, hidden_size),
              nn.Tanh(),
              weight_scale(nn.Linear(hidden_size, 1), 1e-1),
          )
          if self.discrete:
              n_actions = env.action_space.n
              pi = nn.Sequential(
                  nn.Linear(obs_size, hidden_size),
                  nn.Tanh(),
                  weight_scale(nn.Linear(hidden_size, n_actions), 1e-1),
                  SoftmaxCategoricalHead(),
              )
          else:
              action_size = env.action_space.low.size
              pi = nn.Sequential(
                  nn.Linear(obs_size, hidden_size),
                  nn.Tanh(),
                  weight_scale(
                      nn.Linear(hidden_size, action_size), 1e-1
                  ),
                  GaussianHeadWithStateIndependentCovariance(
                      action_size=action_size,
                      var_type="diagonal",
                      var_func=lambda x: torch.exp(2 * x),
                      var_param_init=0,
                  ),
              )
      return pi, v
  
imports: import os
  import tempfile
  import unittest
  import numpy
  import pytest
  import torch
  from torch import nn
  import pfrl
  from pfrl.agents import trpo
  from pfrl.envs.abc import ABC
  from pfrl.experiments import train_agent_batch_with_evaluation, train_agent_with_evaluation
  from pfrl.experiments.evaluator import batch_run_evaluation_episodes, run_evaluation_episodes
  from pfrl.nn import RecurrentSequential
  from pfrl.policies import GaussianHeadWithStateIndependentCovariance, SoftmaxCategoricalHead
  from pfrl.testing import torch_assert_allclose
label_desc: Use image node_id 13 for calling the _TestTRPO obj's underlying member method code with example usage: obj.make_model(env) and returns: pi,  v, layer