code: def is_negative(edge_index, neg_edge_index, size, bipartite):
      adj = torch.zeros(size, dtype=torch.bool)
      neg_adj = torch.zeros(size, dtype=torch.bool)
  
      adj[edge_index[0], edge_index[1]] = True
      neg_adj[neg_edge_index[0], neg_edge_index[1]] = True
  
      if not bipartite:
          arange = torch.arange(size[0])
          assert neg_adj[arange, arange].sum() == 0
  
      return (adj & neg_adj).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 1 for calling a global function with example usage: is_negative(edge_index, neg_edge_index, size, bipartite) and returns: unknown