code: def _create_agent(
      self,
      accepts_per_arm_features: bool,
      exploitation_coefficient: Optional[float] = 10000.0,
      max_exploration_probability_hint: Optional[float] = None,
  ) -> neural_falcon_agent.NeuralFalconAgent:
      if accepts_per_arm_features:
          optimizer = tf.compat.v1.train.GradientDescentOptimizer(
              learning_rate=1e-2
          )
          obs_spec = bandit_spec_utils.create_per_arm_observation_spec(
              global_dim=3,
              per_arm_dim=3,
              max_num_actions=self._num_actions(
                  accepts_per_arm_features
              ),
              add_num_actions_feature=True,
          )
          time_step_spec = ts.time_step_spec(obs_spec)
          reward_net = global_and_arm_feature_network.create_feed_forward_dot_product_network(
              obs_spec,
              global_layers=[3],
              arm_layers=[3],
              activation_fn=tf.keras.activations.linear,
          )
          agent = neural_falcon_agent.NeuralFalconAgent(
              time_step_spec,
              action_spec=self._per_arm_action_spec,
              reward_network=reward_net,
              accepts_per_arm_features=True,
              num_samples_list=[
                  tf.compat.v2.Variable(
                      0, dtype=tf.int64, name="num_samples"
                  )
              ],
              exploitation_coefficient=exploitation_coefficient,
              max_exploration_probability_hint=max_exploration_probability_hint,
              emit_policy_info=(
                  policy_utils.InfoFields.LOG_PROBABILITY,
                  policy_utils.InfoFields.PREDICTED_REWARDS_MEAN,
              ),
              optimizer=optimizer,
          )
      else:
          optimizer = tf.compat.v1.train.GradientDescentOptimizer(
              learning_rate=1.0
          )
          reward_net = DummyNet(
              self._observation_spec, self._action_spec
          )
          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=exploitation_coefficient,
              max_exploration_probability_hint=max_exploration_probability_hint,
              emit_policy_info=(
                  policy_utils.InfoFields.LOG_PROBABILITY,
                  policy_utils.InfoFields.PREDICTED_REWARDS_MEAN,
              ),
              optimizer=optimizer,
          )
      return agent
  
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 4 for calling the NeuralFalconAgentTest obj's underlying member method code with example usage: obj._create_agent(accepts_per_arm_features, exploitation_coefficient, max_exploration_probability_hint) and returns: agent