code: def run_environment_steps_helper(self, env, batch_size, num_steps):
      observation_to_reward_samples = []
      additive_reward_samples = []
      env_time = env._env_time
      observation_to_reward = (
          env._environment_dynamics._current_observation_to_reward
      )
      additive_reward = (
          env._environment_dynamics._current_additive_reward
      )
  
      if tf.executing_eagerly():
          for t in range(0, num_steps):
              ts = env.reset()
              reward = env.step(tf.zeros([batch_size])).reward
  
              (
                  observation_to_reward_sample,
                  additive_reward_sample,
                  env_time_sample,
              ) = self.evaluate(
                  [observation_to_reward, additive_reward, env_time]
              )
              observation_to_reward_samples.append(
                  observation_to_reward_sample
              )
              additive_reward_samples.append(additive_reward_sample)
              self.assertEqual(env_time_sample, (t + 1) * batch_size)
      else:
          ts = env.reset()
          reward = env.step(tf.zeros([batch_size])).reward
  
          for t in range(0, num_steps):
              unused_ts_sample = self.evaluate(ts)
              unused_reward_sample = self.evaluate(reward)
  
              (
                  observation_to_reward_sample,
                  additive_reward_sample,
                  env_time_sample,
              ) = self.evaluate(
                  [observation_to_reward, additive_reward, env_time]
              )
              observation_to_reward_samples.append(
                  observation_to_reward_sample
              )
              additive_reward_samples.append(additive_reward_sample)
              self.assertEqual(env_time_sample, (t + 1) * batch_size)
      return observation_to_reward_samples, additive_reward_samples
  
file_path: agents/tf_agents/bandits/environments/drifting_linear_environment_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
  import tensorflow_probability
  from tf_agents.bandits.environments import drifting_linear_environment
label_desc: Use image node_id 1 for calling the DriftingLinearEnvironmentTest obj's underlying member method code with example usage: obj.run_environment_steps_helper(env, batch_size, num_steps) and returns: observation_to_reward_samples,  additive_reward_samples