code: def initialize(self, session_creator, session_init):
      if (
          not isinstance(session_creator, NewSessionCreator)
          or session_creator.user_provided_config
      ):
          raise ValueError(
              "You are not allowed to set session_creator or session_config for distributed training! "
              "To use a custom session config, pass it to tf.train.Server."
          )
      super(DistributedTrainerBase, self).initialize(
          get_distributed_session_creator(self.server), session_init
      )
  
imports: import multiprocessing
  import os
  import sys
  import tensorflow
  from tensorpack.compat import tfv1
  from ..callbacks import CallbackFactory, RunOp
  from ..graph_builder.distributed import DistributedParameterServerBuilder, DistributedReplicatedBuilder
  from ..graph_builder.training import AsyncMultiGPUBuilder, SyncMultiGPUParameterServerBuilder, SyncMultiGPUReplicatedBuilder
  from ..graph_builder.utils import override_to_local_variable
  from ..input_source import FeedfreeInput, QueueInput
  from ..tfutils import get_global_step_var
  from ..tfutils.common import get_tf_version_tuple
  from ..tfutils.distributed import get_distributed_session_creator
  from ..tfutils.sesscreate import NewSessionCreator
  from ..tfutils.tower import TrainTowerContext
  from ..utils import logger
  from ..utils.argtools import map_arg
  from ..utils.develop import HIDE_DOC, deprecated
  from .tower import SingleCostTrainer
label_desc: Use image node_id 3 for calling the DistributedTrainerBase obj's underlying member method code with example usage: obj.initialize(session_creator, session_init) without return types