parameters: (self,
                 model_name,
                 model_base_path,
                 custom_op_paths="",
                 session_config={})
docstring: Initialize the TensorFlow service by loading SavedModel to the Session.
          
      Args:
        model_name: The name of the model.
        model_base_path: The file path of the model.
      Return:
        None
docstring_summary: Initialize the TensorFlow service by loading SavedModel to the Session.
          
      Args:
        model_name: The name of the model.
        model_base_path: The file path of the model.
      Return:
        None
docstring_tokens: Initialize the TensorFlow service by loading SavedModel to the Session . Args : model_name : The name of the model . model_base_path : The file path of the model . Return : None
function: def __init__(self,
                 model_name,
                 model_base_path,
                 custom_op_paths="",
                 session_config={}):
      """
      Initialize the TensorFlow service by loading SavedModel to the Session.
          
      Args:
        model_name: The name of the model.
        model_base_path: The file path of the model.
      Return:
        None
      """
  
      super(TensorFlowInferenceService, self).__init__()
  
      self.model_name = model_name
      libhdfs_model_base_path = filesystem_util.update_hdfs_prefix_for_libhdfs(
          model_base_path)
      self.model_base_path = libhdfs_model_base_path
      self.model_version_list = []
      self.model_graph_signature = None
      self.model_graph_signature_dict = {}
  
      self.signature_input_tensor_names_map = {}
      self.signature_input_op_names_map = {}
      self.signature_output_tensor_names_map = {}
      self.signature_output_op_names_map = {}
  
      self.platform = "TensorFlow"
      self.session_config = session_config
  
      self.name_signature_map = {}
      self.preprocess_function = None
      self.postprocess_function = None
  
      if self.model_base_path == "":
        raise Exception("The model base path is empty")
      if self.model_base_path.startswith("/") or self.model_base_path.startswith(
          "./"):
        if not os.path.exists(self.model_base_path):
          raise Exception("Local model path does not exist: {}".format(
              self.model_base_path))
  
      if custom_op_paths != "":
        self.load_custom_op(custom_op_paths)
  
      self.version_session_map = {}
      self.profiler_map = {}
  
      self.should_stop_all_threads = False
  
      # Register the signals to exist
      signal.signal(signal.SIGTERM, self.stop_all_threads)
      signal.signal(signal.SIGINT, self.stop_all_threads)
  
      model_versions = self.get_all_model_versions()
      for model_version in model_versions:
        self.load_saved_model_version(model_version)
  
      self.init_model_signature()
function_tokens: def __init__ ( self , model_name , model_base_path , custom_op_paths = "" , session_config = { } ) : super ( TensorFlowInferenceService , self ) . __init__ ( ) self . model_name = model_name libhdfs_model_base_path = filesystem_util . update_hdfs_prefix_for_libhdfs ( model_base_path ) self . model_base_path = libhdfs_model_base_path self . model_version_list = [ ] self . model_graph_signature = None self . model_graph_signature_dict = { } self . signature_input_tensor_names_map = { } self . signature_input_op_names_map = { } self . signature_output_tensor_names_map = { } self . signature_output_op_names_map = { } self . platform = "TensorFlow" self . session_config = session_config self . name_signature_map = { } self . preprocess_function = None self . postprocess_function = None if self . model_base_path == "" : raise Exception ( "The model base path is empty" ) if self . model_base_path . startswith ( "/" ) or self . model_base_path . startswith ( "./" ) : if not os . path . exists ( self . model_base_path ) : raise Exception ( "Local model path does not exist: {}" . format ( self . model_base_path ) ) if custom_op_paths != "" : self . load_custom_op ( custom_op_paths ) self . version_session_map = { } self . profiler_map = { } self . should_stop_all_threads = False # Register the signals to exist signal . signal ( signal . SIGTERM , self . stop_all_threads ) signal . signal ( signal . SIGINT , self . stop_all_threads ) model_versions = self . get_all_model_versions ( ) for model_version in model_versions : self . load_saved_model_version ( model_version ) self . init_model_signature ( )