code: def _build_multigpu_gan_trainer(self, input, model, num_gpu):
      assert num_gpu > 1
      raw_devices = ["/gpu:{}".format(k) for k in range(num_gpu)]
  
      # Build the graph with multi-gpu replication
      def get_cost(*inputs):
          model.build_graph(*inputs)
          return [model.d_loss, model.g_loss]
  
      self.tower_func = TowerFunc(get_cost, model.get_input_signature())
      devices = [
          LeastLoadedDeviceSetter(d, raw_devices) for d in raw_devices
      ]
      cost_list = DataParallelBuilder.call_for_each_tower(
          list(range(num_gpu)),
          lambda: self.tower_func(*input.get_input_tensors()),
          devices,
      )
      # For simplicity, average the cost here. It might be faster to average the gradients
      with tf.name_scope("optimize"):
          d_loss = tf.add_n([x[0] for x in cost_list]) * (1.0 / num_gpu)
          g_loss = tf.add_n([x[1] for x in cost_list]) * (1.0 / num_gpu)
  
          opt = model.get_optimizer()
          # run one d_min after one g_min
          g_min = opt.minimize(
              g_loss,
              var_list=model.g_vars,
              colocate_gradients_with_ops=True,
              name="g_op",
          )
          with tf.control_dependencies([g_min]):
              d_min = opt.minimize(
                  d_loss,
                  var_list=model.d_vars,
                  colocate_gradients_with_ops=True,
                  name="d_op",
              )
      # Define the training iteration
      self.train_op = d_min
  
imports: import numpy
  import tensorflow
  from tensorpack import BatchNorm, DataFlow, ModelDescBase, StagingInput, TowerTrainer, argscope
  from tensorpack.graph_builder import DataParallelBuilder, LeastLoadedDeviceSetter
  from tensorpack.tfutils.summary import add_moving_summary
  from tensorpack.tfutils.tower import TowerContext, TowerFunc
  from tensorpack.utils import logger
  from tensorpack.utils.argtools import memoized_method
label_desc: Use image node_id 3 for calling the GANTrainer obj's underlying member method code with example usage: obj._build_multigpu_gan_trainer(input, model, num_gpu) and returns: list