code: def rand_csc_graph(N, density, bidirection_edge=False):
      adj = sp.random(N, N, density)
      if bidirection_edge:
          adj = adj + adj.T
      adj = adj.tocsc()
  
      indptr = torch.LongTensor(adj.indptr)
      indices = torch.LongTensor(adj.indices)
  
      graph = gb.fused_csc_sampling_graph(indptr, indices)
  
      return graph
  
file_path: dgl/tests/python/pytorch/graphbolt/gb_test_utils.py
imports: import os
  import dgl
  import dgl.graphbolt
  import numpy
  import pandas
  import scipy.sparse
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: rand_csc_graph(N, density, bidirection_edge) and returns: graph