code: def test_needs_reset(self):
      outdir = tempfile.mkdtemp()
  
      agent = mock.Mock()
      env = mock.Mock()
      # First episode: 0 -> 1 -> 2 -> 3 (reset)
      # Second episode: 4 -> 5 -> 6 -> 7 (done)
      env.reset.side_effect = [("state", 0), ("state", 4)]
      env.step.side_effect = [
          (("state", 1), 0, False, {}),
          (("state", 2), 0, False, {}),
          (("state", 3), 0, False, {"needs_reset": True}),
          (("state", 5), -0.5, False, {}),
          (("state", 6), 0, False, {}),
          (("state", 7), 1, True, {}),
      ]
  
      counter = mp.Value("i", 0)
      episodes_counter = mp.Value("i", 0)
      stop_event = mp.Event()
      exception_event = mp.Event()
      train_loop(
          process_idx=0,
          env=env,
          agent=agent,
          steps=5,
          outdir=outdir,
          counter=counter,
          episodes_counter=episodes_counter,
          stop_event=stop_event,
          exception_event=exception_event,
      )
  
      self.assertEqual(agent.act.call_count, 5)
      self.assertEqual(agent.observe.call_count, 5)
      self.assertEqual(agent.observe.call_count, 5)
      # done=False and reset=True at state 3
      self.assertFalse(agent.observe.call_args_list[2][0][2])
      self.assertTrue(agent.observe.call_args_list[2][0][3])
  
      self.assertEqual(env.reset.call_count, 2)
      self.assertEqual(env.step.call_count, 5)
  
file_path: pfrl/tests/experiments_tests/test_train_agent_async.py
imports: import os
  import tempfile
  import unittest
  from unittest import mock
  import pytest
  import torch.multiprocessing
  import pfrl
  from pfrl.experiments.train_agent_async import train_loop
label_desc: Use image node_id 1 for calling the TestTrainLoop obj's underlying member method code with example usage: obj.test_needs_reset() without return types