code: def edge_split_test(g, force_even, etype="_E"):
      gpb = g.get_partition_book()
  
      selected_edges_dist_tensor = dgl.distributed.DistTensor(
          [g.num_edges(etype)], F.uint8, init_func=rand_init
      )
  
      edges = edge_split(
          selected_edges_dist_tensor,
          gpb,
          etype=etype,
          force_even=force_even,
      )
      g.barrier()
  
      selected_edges_dist_tensor[edges] = F.astype(
          F.zeros_like(edges), selected_edges_dist_tensor.dtype
      )
      g.barrier()
  
      if g.rank() == 0:
          batched_assert_zero(
              selected_edges_dist_tensor, g.num_edges(etype)
          )
  
      g.barrier()
  
imports: import json
  import os
  from itertools import product
  import dgl
  import dgl.backend
  import numpy
  from dgl.distributed import edge_split, load_partition_book, node_split
label_desc: Use image node_id 6 for calling a global function with example usage: edge_split_test(g, force_even, etype) without return types