code: def testLargeNumSamples(self, set_max_exploration_probability_hint):
      # With very large numbers of samples and a positive learning rate, the
      # sampled actions will be greedy.
      if set_max_exploration_probability_hint:
          policy = self._create_policy(
              use_mask=False,
              max_exploration_probability_hint=0.05,
              num_samples_list=[
                  tf.compat.v2.Variable(
                      int(1e10), dtype=tf.int32, name="num_samples_0"
                  ),
                  tf.compat.v2.Variable(
                      int(1e10), dtype=tf.int32, name="num_samples_1"
                  ),
                  tf.compat.v2.Variable(
                      int(1e10), dtype=tf.int32, name="num_samples_2"
                  ),
              ],
          )
      else:
          policy = self._create_policy(
              use_mask=False,
              exploitation_coefficient=0.1,
              num_samples_list=[
                  tf.compat.v2.Variable(
                      int(1e10), dtype=tf.int32, name="num_samples_0"
                  ),
                  tf.compat.v2.Variable(
                      int(1e10), dtype=tf.int32, name="num_samples_1"
                  ),
                  tf.compat.v2.Variable(
                      int(1e10), dtype=tf.int32, name="num_samples_2"
                  ),
              ],
          )
      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())
      p_info = self.evaluate(action_step.info)
      # Check the log probabilities in the policy info are greedy.
      self.assertAllClose(p_info.log_probability, [0.0, 0.0], atol=1e-3)
      actions = self.evaluate(action_step.action)
      self.assertAllEqual(actions, [1, 2])
  
imports: from typing import List, Optional, Set
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.bandits.policies import falcon_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 8 for calling the FalconRewardPredictionPolicyTest obj's underlying member method code with example usage: obj.testLargeNumSamples(set_max_exploration_probability_hint) without return types