code: def testAdvantagesAreCorrectlyComputed(
      self, batch_size, num_time_steps, td_lambda
  ):
      rewards = np.random.rand(num_time_steps, batch_size).astype(
          np.float32
      )
      discounts = np.random.rand(num_time_steps, batch_size).astype(
          np.float32
      )
      values = np.random.rand(num_time_steps, batch_size).astype(
          np.float32
      )
      final_value = np.random.rand(batch_size).astype(np.float32)
      ground_truth = _naive_gae_as_ground_truth(
          discounts=discounts,
          rewards=rewards,
          values=values,
          final_value=final_value,
          td_lambda=td_lambda,
      )
  
      advantages = value_ops.generalized_advantage_estimation(
          discounts=discounts,
          rewards=rewards,
          values=values,
          final_value=final_value,
          td_lambda=td_lambda,
      )
  
      self.assertAllClose(advantages, ground_truth)
  
file_path: agents/tf_agents/utils/value_ops_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.utils import value_ops
label_desc: Use image node_id 1 for calling the GeneralizedAdvantageEstimationTest obj's underlying member method code with example usage: obj.testAdvantagesAreCorrectlyComputed(batch_size, num_time_steps, td_lambda) without return types