code: def testZeroNumSamples(
      self, testcase_name, mask, set_max_exploration_probability_hint
  ):
      del testcase_name
      # With number of samples being 0, the sampling probabiity will be uniform.
      if set_max_exploration_probability_hint:
          policy = self._create_policy(
              use_mask=(mask is not None),
              max_exploration_probability_hint=0.01,
              num_samples_list=[
                  tf.compat.v2.Variable(
                      0, dtype=tf.int32, name="num_samples_0"
                  ),
                  tf.compat.v2.Variable(
                      0, dtype=tf.int32, name="num_samples_1"
                  ),
                  tf.compat.v2.Variable(
                      0, dtype=tf.int32, name="num_samples_2"
                  ),
              ],
          )
      else:
          policy = self._create_policy(
              use_mask=(mask is not None),
              exploitation_coefficient=100.0,
              num_samples_list=[
                  tf.compat.v2.Variable(
                      0, dtype=tf.int32, name="num_samples_0"
                  ),
                  tf.compat.v2.Variable(
                      0, dtype=tf.int32, name="num_samples_1"
                  ),
                  tf.compat.v2.Variable(
                      0, dtype=tf.int32, name="num_samples_2"
                  ),
              ],
          )
      batch_size = 3000
      if mask is None:
          observations = tf.constant(
              [[1, 2]] * batch_size, dtype=tf.float32
          )
      else:
          observations = (
              tf.constant([[1, 2]] * batch_size, dtype=tf.float32),
              tf.constant([mask] * batch_size, dtype=tf.float32),
          )
      time_step = ts.restart(observations, batch_size=batch_size)
      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 uniform and the
      # empirical distribution of the chosen arms is uniform.
      actions = self.evaluate(action_step.action)
      if mask is None:
          self.assertAllClose(
              p_info.log_probability,
              tf.math.log([1.0 / 3] * batch_size),
          )
          self._check_uniform_actions(
              actions=actions, allowed_actions=[0, 1, 2]
          )
      else:
          self.assertAllClose(
              p_info.log_probability,
              tf.math.log([1.0 / np.sum(mask)] * batch_size),
          )
          self._check_uniform_actions(
              actions=actions, allowed_actions=np.nonzero(mask)[0]
          )
  
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 7 for calling the FalconRewardPredictionPolicyTest obj's underlying member method code with example usage: obj.testZeroNumSamples(testcase_name, mask, set_max_exploration_probability_hint) without return types