code: def train():
      # assign GPUs for training & inference
      num_gpu = get_num_gpu()
      global PREDICTOR_THREAD
      if num_gpu > 0:
          if num_gpu > 1:
              # use half gpus for inference
              predict_tower = list(range(num_gpu))[-num_gpu // 2 :]
          else:
              predict_tower = [0]
          PREDICTOR_THREAD = (
              len(predict_tower) * PREDICTOR_THREAD_PER_GPU
          )
          train_tower = list(range(num_gpu))[: -num_gpu // 2] or [0]
          logger.info(
              "[Batch-A3C] Train on gpu {} and infer on gpu {}".format(
                  ",".join(map(str, train_tower)),
                  ",".join(map(str, predict_tower)),
              )
          )
      else:
          logger.warn(
              "Without GPU this model will never learn! CPU is only useful for debug."
          )
          PREDICTOR_THREAD = 1
          predict_tower, train_tower = [0], [0]
  
      # setup simulator processes
      name_base = str(uuid.uuid1())[:6]
      prefix = "@" if sys.platform.startswith("linux") else ""
      namec2s = "ipc://{}sim-c2s-{}".format(prefix, name_base)
      names2c = "ipc://{}sim-s2c-{}".format(prefix, name_base)
      procs = [
          MySimulatorWorker(k, namec2s, names2c)
          for k in range(SIMULATOR_PROC)
      ]
      ensure_proc_terminate(procs)
      start_proc_mask_signal(procs)
  
      master = MySimulatorMaster(namec2s, names2c, predict_tower)
      config = TrainConfig(
          model=Model(),
          dataflow=master.get_training_dataflow(),
          callbacks=[
              ModelSaver(),
              ScheduledHyperParamSetter(
                  "learning_rate", [(20, 0.0003), (120, 0.0001)]
              ),
              ScheduledHyperParamSetter("entropy_beta", [(80, 0.005)]),
              master,
              PeriodicTrigger(
                  Evaluator(
                      EVAL_EPISODE, ["state"], ["policy"], get_player
                  ),
                  every_k_epochs=3,
              ),
          ],
          session_creator=sesscreate.NewSessionCreator(
              config=get_default_sess_config(0.5)
          ),
          steps_per_epoch=STEPS_PER_EPOCH,
          session_init=SmartInit(args.load),
          max_epoch=1000,
      )
      trainer = (
          SimpleTrainer()
          if num_gpu == 1
          else AsyncMultiGPUTrainer(train_tower)
      )
      launch_train_with_config(config, trainer)
  
imports: import argparse
  import cv2
  import gym
  import multiprocessing
  import numpy
  import os
  import sys
  import uuid
  import tensorflow
  from six.moves import queue
  from concurrent import futures
  from tensorpack import *
  from tensorpack.tfutils.gradproc import MapGradient, SummaryGradient
  from tensorpack.utils.concurrency import ensure_proc_terminate, start_proc_mask_signal
  from tensorpack.utils.gpu import get_num_gpu
  from tensorpack.utils.serialize import dumps
  from atari_wrapper import FireResetEnv, FrameStack, LimitLength, MapState
  from common import Evaluator, eval_model_multithread, play_n_episodes
  from simulator import SimulatorMaster, SimulatorProcess, TransitionExperience
label_desc: Use image node_id 2 for calling a global function with example usage: train() without return types