code: def make_optimizer(self, env, q_func):
      raise NotImplementedError()
  
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 5 for calling the _TestDQNLike obj's underlying member method code with example usage: obj.make_optimizer(env, q_func) without return types