docstr: Create a TFNet from an TensorFlow saved model
  :param model_path: the path to the SavedModel path
  :param tag: the tag to load in the saved model, default to "serve"
  :param signature: The signature of the SignatureDef that defines inputs
                    and outputs of the graph. TFNet assumes inputs is sorted
                    by their corresponding key in SignatureDef.
  :param inputs: a list input tensor names of this model, you may want to use TensorFlow's
                command line tool to inspect the saved model to find the input tensor
                names e.g. `saved_model_cli show --dir {saved_model_path} --all`
  :param outputs: a list output tensor names of this model, you may want to use TensorFlow's
                command line tool to inspect the saved model to find the output tensor
                names e.g. `saved_model_cli show --dir {saved_model_path} --all`
  :param tf_session_config: an optional tf.ConfigProto object to
                 set the session config in java side.
                 This config does not necessarily be the same with your current session.
                 E.g. sess_config = tf.ConfigProto(inter_op_parallelism_threads=1,
                                                   intra_op_parallelism_threads=1)
                      net = TFNet.from_session(sess, inputs, outputs, sess_config)
  :return: a TFNet
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code: def from_saved_model(
      model_path,
      tag=None,
      signature=None,
      inputs=None,
      outputs=None,
      tf_session_config=None,
      init_op=None,
  ):
      """
      Create a TFNet from an TensorFlow saved model
      :param model_path: the path to the SavedModel path
      :param tag: the tag to load in the saved model, default to "serve"
      :param signature: The signature of the SignatureDef that defines inputs
                        and outputs of the graph. TFNet assumes inputs is sorted
                        by their corresponding key in SignatureDef.
      :param inputs: a list input tensor names of this model, you may want to use TensorFlow's
                    command line tool to inspect the saved model to find the input tensor
                    names e.g. `saved_model_cli show --dir {saved_model_path} --all`
      :param outputs: a list output tensor names of this model, you may want to use TensorFlow's
                    command line tool to inspect the saved model to find the output tensor
                    names e.g. `saved_model_cli show --dir {saved_model_path} --all`
      :param tf_session_config: an optional tf.ConfigProto object to
                     set the session config in java side.
                     This config does not necessarily be the same with your current session.
                     E.g. sess_config = tf.ConfigProto(inter_op_parallelism_threads=1,
                                                       intra_op_parallelism_threads=1)
                          net = TFNet.from_session(sess, inputs, outputs, sess_config)
      :return: a TFNet
      """
      config_bytes = None
      if tf_session_config is not None:
          import tensorflow as tf
  
          assert isinstance(tf_session_config, tf.ConfigProto)
          tf_session_config.use_per_session_threads = True
          config_bytes = bytearray(
              tf_session_config.SerializeToString()
          )
  
      if inputs is None or outputs is None:
          jvalue = callZooFunc(
              "float",
              "createTFNetFromSavedModel",
              model_path,
              tag,
              signature,
              config_bytes,
          )
      else:
          jvalue = callZooFunc(
              "float",
              "createTFNetFromSavedModel",
              model_path,
              tag,
              inputs,
              outputs,
              config_bytes,
              init_op,
          )
      return TFNet(path=None, jvalue=jvalue)
  
imports: import os
  import sys
  import tempfile
  import numpy
  import six
  from pyspark import RDD
  from bigdl.nn.layer import Layer
  from zoo.common import JTensor
  from zoo.common.nncontext import getOrCreateSparkContext
  from zoo.common.utils import callZooFunc
  from zoo.feature.image import ImageSet
  from zoo.tfpark.tf_dataset import TFImageDataset, TFDataset
  import logging
  from zoo.util.utils import to_sample_rdd
label_desc: Use image node_id 6 for calling the TFNet obj's underlying member method code with example usage: obj.from_saved_model(model_path, tag, signature, inputs, outputs, tf_session_config, init_op) and returns: TFNet