code: def make_q_func(self, env):
      n_hidden_channels = 10
      return RecurrentSequential(
          nn.Linear(env.observation_space.low.size, n_hidden_channels),
          nn.ELU(),
          nn.RNN(
              input_size=n_hidden_channels,
              hidden_size=n_hidden_channels,
          ),
          nn.Linear(n_hidden_channels, env.action_space.n),
          q_functions.DiscreteActionValueHead(),
      )
  
imports: import numpy
  import torch.nn
  import torch.optim
  from basetest_training import _TestTraining
  from pfrl import q_functions, replay_buffers
  from pfrl.envs.abc import ABC
  from pfrl.explorers.epsilon_greedy import LinearDecayEpsilonGreedy
  from pfrl.nn import RecurrentSequential
label_desc: Use image node_id 1 for calling the _TestDQNOnDiscretePOABC obj's underlying member method code with example usage: obj.make_q_func(env) and returns: RecurrentSequential