code: def testAllLargeNumSamples(self):
      # When every action has a very large number of samples, the chosen actions
      # should be greedy actions.
      num_samples_list = []
      for k in range(3):
          num_samples_list.append(
              tf.compat.v2.Variable(
                  tf.zeros([], dtype=tf.int32),
                  name="num_samples_{}".format(k),
              )
          )
      num_samples_list[0].assign_add(tf.int32.max - 10)
      num_samples_list[1].assign_add(tf.int32.max - 10)
      num_samples_list[2].assign_add(tf.int32.max - 10)
      policy = boltzmann_reward_policy.BoltzmannRewardPredictionPolicy(
          self._time_step_spec,
          self._action_spec,
          reward_network=DummyNet(self._obs_spec),
          boltzmann_gumbel_exploration_constant=100.0,
          num_samples_list=num_samples_list,
          emit_policy_info=(utils.InfoFields.PREDICTED_REWARDS_MEAN,),
      )
      observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
      time_step = ts.restart(observations, batch_size=2)
      action_step = policy.action(time_step, seed=1)
      # Initialize all variables
      self.evaluate(tf.compat.v1.global_variables_initializer())
      actions = self.evaluate(action_step.action)
      self.assertAllEqual(actions, [1, 2])
  
imports: import numpy
  import tensorflow
  from tf_agents.bandits.policies import boltzmann_reward_prediction_policy
  from tf_agents.networks import network
  from tf_agents.policies import utils
  from tf_agents.specs import tensor_spec
  from tf_agents.trajectories import time_step
  from tf_agents.utils import test_utils
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 6 for calling the BoltzmannRewardPredictionPolicyTest obj's underlying member method code with example usage: obj.testAllLargeNumSamples() without return types