code: def test_edge_dataloader_exclusion_without_all_reverses():
      data_dict = {
          ("A", "AB", "B"): (
              torch.tensor([0, 1]),
              torch.tensor([0, 1]),
          ),
          ("B", "BA", "A"): (
              torch.tensor([0, 1]),
              torch.tensor([0, 1]),
          ),
          ("B", "BC", "C"): (torch.tensor([0]), torch.tensor([0])),
          ("C", "CA", "A"): (
              torch.tensor([0, 1]),
              torch.tensor([0, 1]),
          ),
      }
      g = dgl.heterograph(data_dict=data_dict)
      block_sampler = dgl.dataloading.MultiLayerNeighborSampler(
          fanouts=[1], replace=True
      )
      block_sampler = dgl.dataloading.as_edge_prediction_sampler(
          block_sampler,
          exclude="reverse_types",
          reverse_etypes={"AB": "BA"},
      )
      d = dgl.dataloading.DataLoader(
          graph=g,
          indices={
              "AB": torch.tensor([0]),
              "BC": torch.tensor([0]),
          },
          graph_sampler=block_sampler,
          batch_size=2,
          shuffle=True,
          drop_last=False,
          num_workers=0,
          device=F.ctx(),
          use_ddp=False,
      )
  
      next(iter(d))
  
imports: import os
  import unittest
  from collections.abc import Iterator, Mapping
  from functools import partial
  import backend
  import dgl
  import dgl.ops
  import numpy
  import pytest
  import torch
  import torch.distributed
  import torch.multiprocessing
  from utils import parametrize_idtype
label_desc: Use image node_id 18 for calling a global function with example usage: test_edge_dataloader_exclusion_without_all_reverses() without return types