class_docstr: Indexing edges for fast graph queries
  
  Sorted edges and represent edges in compressed style like csc_matrix or csr_matrix.
  
  Args:
      u: A list of node id to be compressed.
      v: A list of node id that are connected with u.
      num_nodes: The exactive number of nodes.
class_docstr_tok: ["Indexing","edges","for","fast","graph","queries","Sorted","edges","and","represent","edges","in","compressed","style","like","csc_matrix","or","csr_matrix",".","Args",":","u",":","A","list","of","node","id","to","be","compressed",".","v",":","A","list","of","node","id","that","are","connected","with","u.","num_nodes",":","The","exactive","number","of","nodes","."]
code: def __init__(self, u, v, num_nodes):
      (
          self._degree,
          self._sorted_v,
          self._sorted_u,
          self._sorted_eid,
          self._indptr,
      ) = graph_kernel.build_index(u, v, num_nodes)
  
imports: import os
  import numpy
  import pickle
  import time
  import pgl.graph_kernel
  from collections import defaultdict
label_desc: Use image node_id 1 to create a new EdgeIndex object from inherited base classes: object with example: obj = EdgeIndex(u, v, num_nodes)