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 = FCBNDeterministicPolicy(
          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,
          bound_action=True,
      )
  
      q_func = FCBNLateActionSAQFunction(
          n_dim_obs=n_dim_obs,
          n_dim_action=n_dim_action,
          n_hidden_layers=2,
          n_hidden_channels=n_hidden_channels,
      )
  
      return DDPGModel(policy=policy, q_func=q_func)
  
imports: from chainer import optimizers
  import numpy
  from chainerrl.agents.ddpg import DDPGModel
  from chainerrl.envs.abc import ABC
  from chainerrl.explorers.epsilon_greedy import LinearDecayEpsilonGreedy
  from chainerrl.policy import FCBNDeterministicPolicy
  from chainerrl.policy import FCLSTMDeterministicPolicy
  from chainerrl.q_functions import FCBNLateActionSAQFunction
  from chainerrl.q_functions import FCLSTMSAQFunction
  from chainerrl import replay_buffer
  from basetest_training import _TestTraining
label_desc: Use image node_id 1 for calling the _TestDDPGOnContinuousABC obj's underlying member method code with example usage: obj.make_model(env) and returns: DDPGModel