code: def testLossNotMatching(self):
      class MyAgentWithLossNotMatching(MyAgent):
          def _loss(
              self, experience, weights=None, extra=None, training=False
          ):
              return tf_agent.LossInfo(loss=(), extra=(experience, ()))
  
      agent = MyAgentWithLossNotMatching()
      extra = tf.ones(shape=[3, 4], dtype=tf.float32)
      experience = tf.nest.map_structure(
          lambda x: x[tf.newaxis, ...],
          trajectory.from_episode(
              observation={"obs": tf.constant([1.0])},
              action=(),
              policy_info=(),
              reward=tf.constant([1.0]),
          ),
      )
  
      with self.assertRaisesRegex(
          ValueError,
          r".*`LossInfo` from train\(\) and `LossInfo` from loss\(\) do not have "
          "matching structures.*",
      ):
          test_util.test_loss_and_train_output(
              test=self,
              expect_equal_loss_values=True,
              agent=agent,
              experience=experience,
              extra=extra,
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import copy
  import numpy
  import tensorflow
  from tf_agents.agents import data_converter
  from tf_agents.agents import test_util
  from tf_agents.agents import tf_agent
  from tf_agents.policies import random_tf_policy
  from tf_agents.specs import array_spec
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import test_utils
label_desc: Use image node_id 5 for calling the TFAgentTest obj's underlying member method code with example usage: obj.testLossNotMatching() and returns: tf_agent