code: def test_batched_negative_sampling():
      edge_index = torch.as_tensor([[0, 0, 1, 2], [0, 1, 2, 3]])
      edge_index = torch.cat([edge_index, edge_index + 4], dim=1)
      batch = torch.tensor([0, 0, 0, 0, 1, 1, 1, 1])
  
      neg_edge_index = batched_negative_sampling(edge_index, batch)
      assert neg_edge_index.size(1) <= edge_index.size(1)
  
      adj = torch.zeros(8, 8, dtype=torch.bool)
      adj[edge_index[0], edge_index[1]] = True
      neg_adj = torch.zeros(8, 8, dtype=torch.bool)
      neg_adj[neg_edge_index[0], neg_edge_index[1]] = True
  
      assert (adj & neg_adj).sum() == 0
      assert (adj | neg_adj).sum() == edge_index.size(
          1
      ) + neg_edge_index.size(1)
      assert neg_adj[:4, 4:].sum() == 0
      assert neg_adj[4:, :4].sum() == 0
  
file_path: pytorch_geometric/test/utils/test_negative_sampling.py
imports: import torch
  from torch_geometric.utils import batched_negative_sampling, contains_self_loops, is_undirected, negative_sampling, structured_negative_sampling, structured_negative_sampling_feasible, to_undirected
  from torch_geometric.utils._negative_sampling import edge_index_to_vector, vector_to_edge_index
label_desc: Use image node_id 5 for calling a global function with example usage: test_batched_negative_sampling() without return types