code: def test_bipartite_negative_sampling():
      edge_index = torch.as_tensor([[0, 0, 1, 2], [0, 1, 2, 3]])
  
      neg_edge_index = negative_sampling(edge_index, num_nodes=(3, 4))
      assert neg_edge_index.size(1) == edge_index.size(1)
      assert is_negative(
          edge_index, neg_edge_index, (3, 4), bipartite=True
      )
  
      neg_edge_index = negative_sampling(
          edge_index, num_nodes=(3, 4), num_neg_samples=2
      )
      assert neg_edge_index.size(1) == 2
      assert is_negative(
          edge_index, neg_edge_index, (3, 4), bipartite=True
      )
  
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 4 for calling a global function with example usage: test_bipartite_negative_sampling() without return types