code: def main(unused_argv):
      tf.compat.v1.enable_v2_behavior()  # The trainer only runs with V2 enabled.
  
      data_path = FLAGS.data_path
      if not data_path:
          raise ValueError(
              "Please specify the location of the data file."
          )
      if FLAGS.per_arm:
          env = movielens_per_arm_py_environment.MovieLensPerArmPyEnvironment(
              data_path,
              RANK_K,
              BATCH_SIZE,
              num_actions=NUM_ACTIONS,
              csv_delimiter="\t",
          )
      else:
          env = movielens_py_environment.MovieLensPyEnvironment(
              data_path,
              RANK_K,
              BATCH_SIZE,
              num_movies=NUM_ACTIONS,
              csv_delimiter="\t",
          )
      environment = tf_py_environment.TFPyEnvironment(env)
  
      optimal_reward_fn = functools.partial(
          environment_utilities.compute_optimal_reward_with_movielens_environment,
          environment=environment,
      )
  
      optimal_action_fn = functools.partial(
          environment_utilities.compute_optimal_action_with_movielens_environment,
          environment=environment,
      )
  
      if FLAGS.agent == "LinUCB":
          agent = lin_ucb_agent.LinearUCBAgent(
              time_step_spec=environment.time_step_spec(),
              action_spec=environment.action_spec(),
              tikhonov_weight=0.001,
              alpha=AGENT_ALPHA,
              dtype=tf.float32,
              accepts_per_arm_features=FLAGS.per_arm,
          )
      elif FLAGS.agent == "LinTS":
          agent = lin_ts_agent.LinearThompsonSamplingAgent(
              time_step_spec=environment.time_step_spec(),
              action_spec=environment.action_spec(),
              dtype=tf.float32,
              accepts_per_arm_features=FLAGS.per_arm,
          )
      elif FLAGS.agent == "epsGreedy":
          if FLAGS.per_arm:
              network = global_and_arm_feature_network.create_feed_forward_dot_product_network(
                  environment.time_step_spec().observation,
                  global_layers=LAYERS,
                  arm_layers=LAYERS,
              )
          else:
              network = q_network.QNetwork(
                  input_tensor_spec=environment.time_step_spec().observation,
                  action_spec=environment.action_spec(),
                  fc_layer_params=LAYERS,
              )
          agent = eps_greedy_agent.NeuralEpsilonGreedyAgent(
              time_step_spec=environment.time_step_spec(),
              action_spec=environment.action_spec(),
              reward_network=network,
              optimizer=tf.compat.v1.train.AdamOptimizer(
                  learning_rate=LR
              ),
              epsilon=EPSILON,
              emit_policy_info="predicted_rewards_mean",
              info_fields_to_inherit_from_greedy=[
                  "predicted_rewards_mean"
              ],
          )
      elif FLAGS.agent == "DropoutTS":
          train_step_counter = (
              tf.compat.v1.train.get_or_create_global_step()
          )
  
          def dropout_fn():
              return tf.math.maximum(
                  tf.math.reciprocal_no_nan(
                      1.01 + tf.cast(train_step_counter, tf.float32)
                  ),
                  0.0003,
              )
  
          agent = dropout_ts_agent.DropoutThompsonSamplingAgent(
              time_step_spec=environment.time_step_spec(),
              action_spec=environment.action_spec(),
              dropout_rate=dropout_fn,
              network_layers=LAYERS,
              optimizer=tf.compat.v1.train.AdamOptimizer(
                  learning_rate=LR
              ),
          )
  
      regret_metric = tf_bandit_metrics.RegretMetric(optimal_reward_fn)
      suboptimal_arms_metric = tf_bandit_metrics.SuboptimalArmsMetric(
          optimal_action_fn
      )
  
      trainer.train(
          root_dir=FLAGS.root_dir,
          agent=agent,
          environment=environment,
          training_loops=TRAINING_LOOPS,
          steps_per_loop=STEPS_PER_LOOP,
          additional_metrics=[regret_metric, suboptimal_arms_metric],
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import os
  from absl import app
  from absl import flags
  import tensorflow
  from tf_agents.bandits.agents import dropout_thompson_sampling_agent
  from tf_agents.bandits.agents import lin_ucb_agent
  from tf_agents.bandits.agents import linear_thompson_sampling_agent
  from tf_agents.bandits.agents import neural_epsilon_greedy_agent
  from tf_agents.bandits.agents.examples.v2 import trainer
  from tf_agents.bandits.environments import environment_utilities
  from tf_agents.bandits.environments import movielens_per_arm_py_environment
  from tf_agents.bandits.environments import movielens_py_environment
  from tf_agents.bandits.metrics import tf_metrics
  from tf_agents.bandits.networks import global_and_arm_feature_network
  from tf_agents.environments import tf_py_environment
  from tf_agents.networks import q_network
label_desc: Use image node_id 1 for calling a global function with example usage: main(unused_argv) and returns: tf