code: def testUntrainedPolicy(self):
      reward_net = DummyNet(self._observation_spec, self._action_spec)
      optimizer = tf.compat.v1.train.GradientDescentOptimizer(
          learning_rate=0.1
      )
      agent = neural_falcon_agent.NeuralFalconAgent(
          self._time_step_spec,
          self._action_spec,
          reward_network=reward_net,
          num_samples_list=[
              tf.compat.v2.Variable(
                  0, dtype=tf.int64, name="num_samples_0"
              ),
              tf.compat.v2.Variable(
                  0, dtype=tf.int64, name="num_samples_1"
              ),
              tf.compat.v2.Variable(
                  0, dtype=tf.int64, name="num_samples_2"
              ),
          ],
          exploitation_coefficient=10000.0,
          emit_policy_info=(policy_utils.InfoFields.LOG_PROBABILITY,),
          optimizer=optimizer,
      )
  
      # An untrained policy is expected to sample actions uniformly at random.
      batch_size = 3000
      observations = tf.constant(
          [[1, 2, 3]] * batch_size, dtype=tf.float32
      )
      time_step = ts.restart(observations, batch_size=batch_size)
      # Untrained policy samples actions uniformly at random.
      action_step = agent.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.
      self.assertAllEqual(
          p_info.log_probability, tf.math.log([1.0 / 3] * batch_size)
      )
      # Check the empirical distribution of the chosen arms is uniform.
      actions = self.evaluate(action_step.action)
      self._check_uniform_actions(actions, batch_size)
  
imports: from typing import Optional
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tf_agents.bandits.agents import neural_falcon_agent
  from tf_agents.bandits.networks import global_and_arm_feature_network
  from tf_agents.bandits.specs import utils
  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.trajectories import trajectory
  from tf_agents.typing import types
label_desc: Use image node_id 9 for calling the NeuralFalconAgentTest obj's underlying member method code with example usage: obj.testUntrainedPolicy() without return types