code: def make_model(self, env):
      obs_size = env.observation_space.low.size
      action_size = env.action_space.low.size
      hidden_size = 50
      # Model must be recurrent
      policy = RecurrentSequential(
          nn.Linear(obs_size, hidden_size),
          nn.ReLU(),
          nn.LSTM(input_size=hidden_size, hidden_size=hidden_size),
          nn.Linear(hidden_size, action_size),
          BoundByTanh(
              low=env.action_space.low, high=env.action_space.high
          ),
          DeterministicHead(),
      )
      q_func = RecurrentSequential(
          ConcatObsAndAction(),
          nn.Linear(obs_size + action_size, hidden_size),
          nn.ReLU(),
          nn.LSTM(input_size=hidden_size, hidden_size=hidden_size),
          nn.Linear(hidden_size, 1),
      )
      return policy, q_func
  
imports: import numpy
  import torch
  from basetest_training import _TestTraining
  from torch import nn
  from pfrl import replay_buffers
  from pfrl.envs.abc import ABC
  from pfrl.explorers.epsilon_greedy import LinearDecayEpsilonGreedy
  from pfrl.nn import BoundByTanh, ConcatObsAndAction, RecurrentSequential
  from pfrl.policies import DeterministicHead
label_desc: Use image node_id 1 for calling the _TestDDPGOnContinuousPOABC obj's underlying member method code with example usage: obj.make_model(env) and returns: policy,  q_func