code: def testAgentParametersUpdateWhenTrained(
      self, action_shape: Tuple[int, ...]
  ):
      agent = create_agent(
          action_shape=action_shape,
          eval_mode=False,
          min_replay_history=0,
          update_period=1,
      )
      observation = np.full(OBSERVATION_SHAPE, 1.0, dtype=np.float32)
  
      agent._replay.sample_transition_batch = mock.MagicMock(
          return_value=get_mock_batch(action_shape)
      )
      agent._replay.add_count = 10
  
      agent_before_training = copy.deepcopy(agent)
      agent.begin_episode(observation)
      agent_after_one_step = copy.deepcopy(agent)
      agent.step(reward=0.0, observation=observation)
  
      self.assertAgentParametersNotEqual(
          agent_before_training, agent_after_one_step
      )
      self.assertAgentParametersNotEqual(agent_after_one_step, agent)
  
imports: import copy
  import functools
  import operator
  import random
  from typing import Any, Tuple, Union
  from unittest import mock
  from absl.testing import absltest
  from absl.testing import parameterized
  from dopamine.jax.agents.sac import sac_agent
  import flax
  import gin
  import jax
  import numpy
label_desc: Use image node_id 6 for calling the SacAgentTest obj's underlying member method code with example usage: obj.testAgentParametersUpdateWhenTrained(action_shape) without return types