code: def make_q_func(self, env):
      n_dim_action = env.action_space.low.size
      n_dim_obs = env.observation_space.low.size
      return q_functions.FCQuadraticStateQFunction(
          n_input_channels=n_dim_obs,
          n_dim_action=n_dim_action,
          n_hidden_channels=20,
          n_hidden_layers=2,
          action_space=env.action_space,
      )
  
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 _TestNStepDQNOnContinuousABC obj's underlying member method code with example usage: obj.make_q_func(env) and returns: q_functions