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
  
          assert 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.nn.criterion import Criterion
  from bigdl.nn.layer import Layer
  from bigdl.optim.optimizer import MaxEpoch, EveryEpoch
  from bigdl.util.common import to_list, JavaValue
  from zoo.common.utils import callZooFunc
  from zoo.feature.common import FeatureSet
  from zoo.pipeline.api.keras.engine.topology import to_bigdl_metric, Loss, OptimMethod
  from zoo.pipeline.api.net.utils import find_placeholders, to_bigdl_optim_method, find_tensors
  from zoo.pipeline.estimator import Estimator
  from zoo.util import nest
  from zoo.util.triggers import EveryEpoch
  from zoo.util.triggers import ZooTrigger
  from zoo.tfpark.tf_dataset import TFNdarrayDataset, check_data_compatible
  from zoo.tfpark.tf_dataset import _standarize_feature_label_dataset
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)