code: def make_model(self, env):
      hidden_size = 50
  
      obs_size = env.observation_space.low.size
      v = nn.Sequential(
          nn.Linear(obs_size, hidden_size),
          nn.Tanh(),
          nn.Linear(hidden_size, 1),
      )
  
      if self.discrete:
          n_actions = env.action_space.n
          pi = nn.Sequential(
              nn.Linear(obs_size, hidden_size),
              nn.Tanh(),
              nn.Linear(hidden_size, n_actions),
              SoftmaxCategoricalHead(),
          )
      else:
          action_size = env.action_space.low.size
          pi = nn.Sequential(
              nn.Linear(obs_size, hidden_size),
              nn.Tanh(),
              nn.Linear(hidden_size, action_size),
              GaussianHeadWithStateIndependentCovariance(
                  action_size=action_size,
                  var_type="diagonal",
                  var_func=lambda x: torch.exp(2 * x),
                  var_param_init=0,
              ),
          )
  
      return Branched(pi, v)
  
imports: import os
  import tempfile
  import numpy
  import pytest
  import torch
  from torch import nn
  import pfrl
  from pfrl.agents.a2c import A2C
  from pfrl.envs.abc import ABC
  from pfrl.experiments.evaluator import batch_run_evaluation_episodes
  from pfrl.nn import Branched
  from pfrl.policies import GaussianHeadWithStateIndependentCovariance, SoftmaxCategoricalHead
label_desc: Use image node_id 9 for calling the TestA2C obj's underlying member method code with example usage: obj.make_model(env) and returns: Branched