class_docstr: Initialize the Distributed Graph Server
  
  Args:
      root_dir: the graph data dir 
  
      node_types: the node type configs
  
      edge_types: the edge type configs.
  
      symmetry: whether the edges are symmetry
  
      slots: the node feature slot 
  
      token_slot: for erniesage token slot input
  
      slot_num_for_pull_feature: total slot feature number we should pull
  
      num_parts: the sharded parts of graph data
  
      metapath_split_opt: whether use metapath split optimization
code: def load_infer_node_from_file(self, infer_nodes_path):
      if not infer_nodes_path:
          self.graph.set_node_iter_from_graph(False)
      else:
          # for online learning, maybe we need to change input for dir.
          nodetype2files = helper.generate_files_string(
              self.ntype_list, infer_nodes_path
          )
          self.graph.set_node_iter_from_file(
              nodetype2files, self.root_dir, self.num_parts, False
          )
  
imports: import sys
  import time
  from paddle.fluid.core import GraphGpuWrapper
  from pgl.utils.logger import log
  import helper
  import util
  from place import get_cuda_places
label_desc: Use image node_id 10 for calling the DistGraph obj's underlying member method code with example usage: obj.load_infer_node_from_file(infer_nodes_path) without return types