code: def make_model(self, env):
      n_dim_obs = env.observation_space.low.size
      n_dim_action = env.action_space.low.size
      n_hidden_channels = 50
  
      policy = policies.FCGaussianPolicy(
          n_input_channels=n_dim_obs,
          n_hidden_layers=2,
          n_hidden_channels=n_hidden_channels,
          action_size=n_dim_action,
          min_action=env.action_space.low,
          max_action=env.action_space.high,
      )
  
      q_func = q_function.FCSAQFunction(
          n_dim_obs=n_dim_obs,
          n_dim_action=n_dim_action,
          n_hidden_layers=2,
          n_hidden_channels=n_hidden_channels,
      )
  
      return chainer.Chain(policy=policy, q_function=q_func)
  
imports: import chainer
  from chainer import functions
  from chainer import links
  from chainer import optimizers
  import numpy
  from chainerrl.envs.abc import ABC
  from chainerrl.explorers.epsilon_greedy import LinearDecayEpsilonGreedy
  from chainerrl.links import Sequence
  from chainerrl import policies
  from chainerrl import q_function
  from chainerrl import replay_buffer
  from basetest_training import _TestTraining
label_desc: Use image node_id 1 for calling the _TestPGTOnContinuousABC obj's underlying member method code with example usage: obj.make_model(env) and returns: chainer