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 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 3 for calling the _TestDDPGOnABC obj's underlying member method code with example usage: obj.make_explorer(env) and returns: LinearDecayEpsilonGreedy, a, a