code: def _custom_shapes_test(self, shape, dtype, stack_size):
      self.observation_shape = shape
      self.observation_dtype = dtype
      self.stack_size = stack_size
      self.zero_state = onp.zeros(shape + (stack_size,))
      agent = self._create_test_agent()
      agent.eval_mode = False
      base_observation = onp.ones(self.observation_shape + (1,))
      # We mock the replay buffer to verify how the agent interacts with it.
      agent._replay = test_utils.MockReplayBuffer(is_jax=True)
      # This will reset state and choose a first action.
      agent.begin_episode(base_observation)
      observation = base_observation
  
      expected_state = self.zero_state
      num_steps = 10
      for step in range(1, num_steps + 1):
          # We make observation a multiple of step for testing purposes (to
          # uniquely identify each observation).
          last_observation = observation
          observation = base_observation * step
          self.assertEqual(
              agent.step(reward=1, observation=observation), 0
          )
          stack_pos = step - num_steps - 1
          if stack_pos >= -self.stack_size:
              expected_state[..., stack_pos] = onp.full(
                  self.observation_shape, step
              )
          self.assertEqual(agent._replay.add.call_count, step)
          mock_args, _ = agent._replay.add.call_args
          self.assertTrue(
              onp.array_equal(last_observation[..., 0], mock_args[0])
          )
          self.assertEqual(0, mock_args[1])  # Action selected.
          self.assertEqual(1, mock_args[2])  # Reward received.
          self.assertFalse(mock_args[3])  # is_terminal
      self.assertTrue(onp.array_equal(agent.state, expected_state))
      self.assertTrue(
          onp.array_equal(
              agent._last_observation,
              onp.full(self.observation_shape, num_steps - 1),
          )
      )
      self.assertTrue(
          onp.array_equal(agent._observation, observation[..., 0])
      )
      # We expect one more than num_steps because of the call to begin_episode.
      self.assertEqual(agent.training_steps, num_steps + 1)
      self.assertEqual(agent._replay.add.call_count, num_steps)
  
      agent.end_episode(reward=1)
      self.assertEqual(agent._replay.add.call_count, num_steps + 1)
      mock_args, _ = agent._replay.add.call_args
      self.assertTrue(
          onp.array_equal(observation[..., 0], mock_args[0])
      )
      self.assertEqual(0, mock_args[1])  # Action selected.
      self.assertEqual(1, mock_args[2])  # Reward received.
      self.assertTrue(mock_args[3])
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import shutil
  from absl import flags
  from absl.testing import absltest
  from dopamine.discrete_domains import atari_lib
  from dopamine.jax.agents.dqn import dqn_agent
  from dopamine.utils import test_utils
  from flax import linen
  import gin
  import jax.numpy
  import mock
  import numpy
label_desc: Use image node_id 9 for calling the DQNAgentTest obj's underlying member method code with example usage: obj._custom_shapes_test(shape, dtype, stack_size) without return types