code: def __init__(self, path, config_proto, saver, meta, sess):
      self.saver = saver
      self.meta = meta
      self.export_dir = path
      self.sess = sess
  
      if config_proto is not None:
          import tensorflow as tf
  
          invalidInputError(
              isinstance(config_proto, tf.ConfigProto),
              "session_config should be a tf.ConfigProto",
          )
          config_proto.use_per_session_threads = True
          byte_arr = bytearray(config_proto.SerializeToString())
      else:
          byte_arr = None
  
      super(TFTrainingHelper, self).__init__(
          None, "float", path, byte_arr
      )
  
imports: import json
  import logging
  import os
  import sys
  import tempfile
  from bigdl.dllib.nn.criterion import Criterion
  from bigdl.dllib.nn.layer import Layer
  from bigdl.dllib.optim.optimizer import MaxEpoch, EveryEpoch
  from bigdl.dllib.utils.common import to_list, JavaValue
  from bigdl.dllib.utils.file_utils import callZooFunc
  from bigdl.dllib.feature.common import FeatureSet
  from bigdl.dllib.keras.engine.topology import to_bigdl_metric, Loss, OptimMethod
  from bigdl.dllib.net.utils import find_placeholders, to_bigdl_optim_method, find_tensors
  from bigdl.dllib.estimator.estimator import Estimator
  from bigdl.dllib.utils import nest
  from bigdl.dllib.utils.triggers import EveryEpoch
  from bigdl.dllib.utils.triggers import ZooTrigger
  from bigdl.orca.tfpark.tf_dataset import TFNdarrayDataset, check_data_compatible
  from bigdl.orca.tfpark.tf_dataset import _standarize_feature_label_dataset
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 1 to create a new TFTrainingHelper object from inherited base classes: Layer with example: obj = TFTrainingHelper(path, config_proto, saver, meta, sess)