code: def cross_attention1(self, x1, batch1, x2, batch2):
      results1, results2 = [], []
      for i in range(batch1[-1] + 1):
          x, y = x1[batch1 == i, :], x2[batch2 == i, :]
          att_scores = torch.mm(x, torch.transpose(y, 1, 0))
          attention_x = torch.mm(torch.softmax(att_scores, dim=1), y)
          attention_y = torch.mm(
              torch.transpose(torch.softmax(att_scores, dim=0), 1, 0), x
          )
          results1.append(attention_x)
          results2.append(attention_y)
  
      attentions1 = x1 - torch.cat(results1, dim=0)
      attentions2 = x2 - torch.cat(results2, dim=0)
  
      return attentions1, attentions2
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch_scatter import scatter
  from torch_geometric.nn import global_add_pool, global_mean_pool, global_max_pool, GINConv
label_desc: Use image node_id 6 for calling the GMNConv obj's underlying member method code with example usage: obj.cross_attention1(x1, batch1, x2, batch2) and returns: attentions1,  attentions2