code: def reduce_func(self, nodes):
      h_in = nodes.data["h"]
      h = nodes.mailbox["e"]
  
      vector_field = nodes.mailbox["vector_field"]
  
      h = torch.cat(
          [
              aggregate(h, vector_field, h_in)
              for aggregate in self.aggregators
          ],
          dim=1,
      )
  
      return {"h": h}
  
imports: import argparse
  import random
  import dgl
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from dgl.dataloading import GraphDataLoader
  from ogb.graphproppred import Evaluator
  from ogb.graphproppred.mol_encoder import AtomEncoder
  from preprocessing import prepare_dataset
  from torch.utils.data import Dataset
  from tqdm import tqdm
label_desc: Use image node_id 4 for calling the DGNLayer obj's underlying member method code with example usage: obj.reduce_func(nodes) and returns: dict