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 __init__(
      self,
      root_dir,
      node_types,
      edge_types,
      symmetry,
      slots,
      token_slot,
      slot_num_for_pull_feature,
      num_parts,
      metapath_split_opt=False,
      infer_nodes=None,
      use_weight=False,
  ):
      self.root_dir = root_dir
      self.node_types = node_types
      self.edge_types = edge_types
      self.symmetry = symmetry
      self.slots = slots
      self.token_slot = token_slot
      self.slot_num_for_pull_feature = slot_num_for_pull_feature
      self.num_parts = num_parts
      self.metapath_split_opt = metapath_split_opt
      self.infer_nodes = infer_nodes
      self.use_weight = use_weight
      self.reverse = 1 if self.symmetry else 0
  
      self.etype2files = helper.parse_files(self.edge_types)
      self.etype_list = util.get_all_edge_type(
          self.etype2files, self.symmetry
      )
  
      self.ntype2files = helper.parse_files(self.node_types)
      self.ntype_list = list(self.ntype2files.keys())
      log.info("total etype: %s" % repr(self.etype_list))
      log.info("total ntype: %s" % repr(self.ntype_list))
  
      self.graph = GraphGpuWrapper()
  
      self._setup_graph()
  
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 1 to create a new DistGraph object from inherited base classes: object with example: obj = DistGraph(root_dir, node_types, edge_types, symmetry, slots, token_slot, slot_num_for_pull_feature, num_parts, metapath_split_opt, infer_nodes, use_weight)