code: def testLinearAgentFinalTheta(self, set_example_weights):
      agent, experience, weights = _create_simple_agent_and_data(
          set_example_weights
      )
      self.evaluate(agent.initialize())
      reward = tf.squeeze(experience.reward, axis=-1)
      self.evaluate(agent.train(experience, weights=weights))
      final_theta = self.evaluate(agent.theta)
      self.assertAllClose(tf.shape(final_theta), [1, 1])
      # Because the observation consists of a single constant feature and the
      # agent uses zero regularization for training (`tikhonov_weight` set to 0),
      # the final theta is expected to be the average reward when the weights are
      # unset, and the weighted average reward when the weights are set.
      if weights is None:
          self.assertAllClose(final_theta[0, 0], tf.reduce_mean(reward))
      else:
          self.assertAllClose(
              final_theta[0, 0],
              tf.reduce_sum(weights * reward) / tf.reduce_sum(weights),
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from typing import Optional, Tuple
  from absl import flags
  from absl.testing import parameterized
  import numpy
  import tensorflow
  import tensorflow_probability
  from tf_agents.bandits.agents import lin_ucb_agent
  from tf_agents.bandits.agents import linear_bandit_agent
  from tf_agents.bandits.agents import linear_thompson_sampling_agent
  from tf_agents.bandits.agents import utils
  from tf_agents.bandits.drivers import driver_utils
  from tf_agents.bandits.specs import utils
  from tf_agents.policies import utils
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import policy_step
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
  from tf_agents.utils import common
label_desc: Use image node_id 4 for calling the LinearBanditAgentTest obj's underlying member method code with example usage: obj.testLinearAgentFinalTheta(set_example_weights) without return types