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.
      
code: def numpy(self, inplace=True):
      if not self._is_tensor:
          # already numpy
          return self
  
      if inplace:
          self._sorted_u = self._sorted_u.numpy()
          self._sorted_v = self._sorted_v.numpy()
          self._sorted_eid = self._sorted_eid.numpy()
          self._degree = paddle.to_tensor(self._degree)
          self._indptr = paddle.to_tensor(self._indptr)
          self._is_tensor = False
          return self
      else:
          sorted_v = self._sorted_v.numpy()
          sorted_u = self._sorted_u.numpy()
          sorted_eid = self._sorted_eid.numpy()
          degree = self._degree.numpy()
          indptr = self._indptr.numpy()
          return EdgeIndex.from_index(
              sorted_v=sorted_v,
              sorted_u=sorted_u,
              sorted_eid=sorted_eid,
              indptr=indptr,
              degree=degree,
          )
  
imports: import os
  import json
  import copy
  import numpy
  import paddle
  from paddle.framework import core
  from pgl.utils import op
  import pgl.graph_kernel
  from pgl.utils.helper import check_is_tensor, to_paddle_tensor
label_desc: Use image node_id 10 for calling the EdgeIndex obj's underlying member method code with example usage: obj.numpy(inplace) and returns: self, self, EdgeIndex