code: def read_edges_file(edge_file, edge_data_dict):
      """
      Utility function to read xxx_edges.txt file
  
      Parameters:
      -----------
      edge_file : string
          Graph file for edges in the input graph
  
      Returns:
      --------
      dictionary
          edge data as read from xxx_edges.txt file and columns are stored
          in a dictionary with key-value pairs as column-names and column-data.
      """
      if edge_file == "" or edge_file == None:
          return None
  
      # Read the file from here.
      # <global_src_id> <global_dst_id> <type_eid> <etype> <attributes>
      # global_src_id -- global idx for the source node ... line # in the graph_nodes.txt
      # global_dst_id -- global idx for the destination id node ... line # in the graph_nodes.txt
  
      edge_data_df = csv.read_csv(
          edge_file,
          read_options=pyarrow.csv.ReadOptions(
              autogenerate_column_names=True
          ),
          parse_options=pyarrow.csv.ParseOptions(delimiter=" "),
      )
      edge_data_dict = {}
      edge_data_dict[constants.GLOBAL_SRC_ID] = edge_data_df[
          "f0"
      ].to_numpy()
      edge_data_dict[constants.GLOBAL_DST_ID] = edge_data_df[
          "f1"
      ].to_numpy()
      edge_data_dict[constants.GLOBAL_TYPE_EID] = edge_data_df[
          "f2"
      ].to_numpy()
      edge_data_dict[constants.ETYPE_ID] = edge_data_df["f3"].to_numpy()
      return edge_data_dict
  
imports: import json
  import logging
  import os
  from itertools import cycle
  import constants
  import dgl
  import numpy
  import psutil
  import pyarrow
  import torch
  from dgl.distributed.partition import _dump_part_config
  from pyarrow import csv
label_desc: Use image node_id 11 for calling a global function with example usage: read_edges_file(edge_file, edge_data_dict) and returns: edge_data_dict, None