code: def batch2tensor(batch_adj, batch_feat, node_per_pool_graph):
      """
      transform a batched graph to batched adjacency tensor and node feature tensor
      """
      batch_size = int(batch_adj.size()[0] / node_per_pool_graph)
      adj_list = []
      feat_list = []
      for i in range(batch_size):
          start = i * node_per_pool_graph
          end = (i + 1) * node_per_pool_graph
          adj_list.append(batch_adj[start:end, start:end])
          feat_list.append(batch_feat[start:end, :])
      adj_list = list(map(lambda x: th.unsqueeze(x, 0), adj_list))
      feat_list = list(map(lambda x: th.unsqueeze(x, 0), feat_list))
      adj = th.cat(adj_list, dim=0)
      feat = th.cat(feat_list, dim=0)
  
      return feat, adj
  
label_desc: Use image node_id 1 for calling a global function with example usage: batch2tensor(batch_adj, batch_feat, node_per_pool_graph) and returns: feat,  adj