code: def testMasking(self, dtype):
      if not dtype.is_integer:
          self.skipTest("testMasking only applies to integer dtypes")
  
      batch_size = 1000
  
      action_spec = tensor_spec.BoundedTensorSpec((), dtype, -5, 5)
      time_step_spec, time_step = self.create_time_step()
      time_step = self.create_batch(time_step, batch_size)
  
      # We create a fixed mask here for testing purposes. Normally the mask would
      # be part of the observation.
      mask = [0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0]
      np_mask = np.array(mask)
      tf_mask = tf.constant([mask for _ in range(batch_size)])
  
      policy = random_tf_policy.RandomTFPolicy(
          time_step_spec=time_step_spec,
          action_spec=action_spec,
          emit_log_probability=True,
          observation_and_action_constraint_splitter=lambda obs: (
              obs,
              tf_mask,
          ),
      )
  
      action_step = policy.action(time_step)
      tf.nest.assert_same_structure(action_spec, action_step.action)
  
      # Sample from the policy 1000 times, and ensure that actions considered
      # invalid according to the mask are never chosen.
      step = self.evaluate(action_step)
      action_ = step.action
      self.assertTrue(np.all(action_ >= -5))
      self.assertTrue(np.all(action_ <= 5))
      self.assertAllEqual(
          np_mask[action_ - action_spec.minimum], np.ones([batch_size])
      )
  
      # Ensure that all valid actions occur somewhere within the batch. Because we
      # sample 1000 times, the chance of this failing for any particular action is
      # (2/3)^1000, roughly 1e-176.
      for index in range(action_spec.minimum, action_spec.maximum + 1):
          if np_mask[index - action_spec.minimum]:
              self.assertIn(index, action_)
  
      # With only three valid actions, all of the probabilities should be 1/3.
      self.assertAllClose(
          step.info.log_probability,
          tf.constant(np.log(1.0 / 3), shape=[batch_size]),
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.bandits.specs import utils
  from tf_agents.policies import random_tf_policy
  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 nest_utils
  from tf_agents.utils import test_utils
label_desc: Use image node_id 7 for calling the RandomTFPolicyTest obj's underlying member method code with example usage: obj.testMasking(dtype) without return types