code: def aggregate(self, xs):
      if self.aggr == "mean":
          x = torch.stack(xs, dim=-1)
          return x.mean(dim=-1)
  
      elif self.aggr == "attn":
          N = xs[0].shape[0]  # Number of nodes for that node type
          M = len(xs)  # Number of message types for that node type
  
          x = torch.cat(xs, dim=0).view(M, N, -1)  # M * N * D
          z = self.attn_proj(x).view(M, N)  # M * N * 1
          z = z.mean(1)  # M * 1
          alpha = torch.softmax(z, dim=0)  # M * 1
  
          # Store the attention result to self.alpha as np array
          self.alpha = alpha.view(-1).data.cpu().numpy()
  
          alpha = alpha.view(M, 1, 1)
          x = x * alpha
          return x.sum(dim=0)
  
imports: import copy
  import torch
  import argparse
  import deepsnap
  import numpy
  import torch.nn
  import torch.nn.functional
  import torch_geometric.nn
  from sklearn.metrics import f1_score
  from deepsnap.hetero_gnn import forward_op, HeteroSAGEConv
  from deepsnap.hetero_graph import HeteroGraph
  from torch_sparse import SparseTensor, matmul
label_desc: Use image node_id 4 for calling the HeteroGNNWrapperConv obj's underlying member method code with example usage: obj.aggregate(xs) and returns: x, x