code: def test(self):
      steps = self.steps
  
      outdir = tempfile.mkdtemp()
  
      agent = mock.Mock()
      agent.batch_act_and_train.side_effect = [
          [1] * self.num_envs
      ] * 1000
  
      def make_env():
          env = mock.Mock()
          env.reset.side_effect = [("state", 0)] * 1000
          if self.max_episode_len is None:
              # Episodic env that terminates after 5 actions
              env.step.side_effect = [
                  (("state", 1), 0, False, {}),
                  (("state", 2), 0, False, {}),
                  (("state", 3), -0.5, False, {}),
                  (("state", 4), 0, False, {}),
                  (("state", 5), 1, True, {}),
              ] * 1000
          else:
              # Continuing env
              env.step.side_effect = [
                  (("state", 1), 0, False, {}),
              ] * 1000
          return env
  
      vec_env = chainerrl.envs.SerialVectorEnv(
          [make_env() for _ in range(self.num_envs)]
      )
  
      hook = mock.Mock()
  
      chainerrl.experiments.train_agent_batch(
          agent=agent,
          env=vec_env,
          steps=steps,
          outdir=outdir,
          max_episode_len=self.max_episode_len,
          step_hooks=[hook],
      )
  
      iters = math.ceil(steps / self.num_envs)
      self.assertEqual(agent.batch_act_and_train.call_count, iters)
      self.assertEqual(agent.batch_observe_and_train.call_count, iters)
  
      for env in vec_env.envs:
          if self.max_episode_len is None:
              if self.num_envs == 1:
                  if self.steps == 6:
                      # In the beginning and after 5 iterations
                      self.assertEqual(env.reset.call_count, 2)
                  else:
                      assert steps == 5
                      # Only in the beginning. While the last state is
                      # terminal, env.reset should not be called because
                      # training is complete.
                      self.assertEqual(env.reset.call_count, 1)
              elif self.num_envs == 2:
                  # Only in the beginning
                  self.assertEqual(env.reset.call_count, 1)
              else:
                  assert False
          elif self.max_episode_len == 2:
              if self.num_envs == 1:
                  # In the beginning, after 2 and 4 iterations
                  self.assertEqual(env.reset.call_count, 3)
              elif self.num_envs == 2:
                  # In the beginning, after 2 iterations
                  self.assertEqual(env.reset.call_count, 2)
              else:
                  assert False
          self.assertEqual(env.step.call_count, iters)
  
      if steps % self.num_envs == 0:
          self.assertEqual(hook.call_count, steps)
      else:
          self.assertEqual(hook.call_count, self.num_envs * iters)
  
      # A hook receives (env, agent, step)
      for i, call in enumerate(hook.call_args_list):
          args, kwargs = call
          self.assertEqual(args[0], vec_env)
          self.assertEqual(args[1], agent)
          # step starts with 1
          self.assertEqual(args[2], i + 1)
  
file_path: chainerrl/tests/experiments_tests/test_train_agent_batch.py
imports: import math
  import tempfile
  import unittest
  from unittest import mock
  from chainer import testing
  import chainerrl
label_desc: Use image node_id 1 for calling the TestTrainAgentBatch obj's underlying member method code with example usage: obj.test() and returns: env