code: def make_explorer(self, env):
      def random_action_func():
          a = env.action_space.sample()
          if isinstance(a, np.ndarray):
              return a.astype(np.float32)
          else:
              return a
  
      return LinearDecayEpsilonGreedy(
          1.0, 0.2, 1000, random_action_func
      )
  
imports: import chainer
  from chainer import functions
  from chainer import links
  from chainer import optimizers
  import numpy
  from chainerrl.envs.abc import ABC
  from chainerrl.explorers.epsilon_greedy import LinearDecayEpsilonGreedy
  from chainerrl.links import Sequence
  from chainerrl import policies
  from chainerrl import q_function
  from chainerrl import replay_buffer
  from basetest_training import _TestTraining
label_desc: Use image node_id 3 for calling the _TestPGTOnABC obj's underlying member method code with example usage: obj.make_explorer(env) and returns: LinearDecayEpsilonGreedy, a, a