code: def forward(self, blocks, h):
      for layer, block in zip(self.convs, blocks):
          h_dst = h[: block.num_nodes("DST/" + block.ntypes[0])]
          h = layer(block, (h, h_dst), block.edata["weights"])
      return h
  
imports: import argparse
  import pickle
  import time
  import dgl
  import dgl.function
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.data import DataLoader, IterableDataset
  from ..None import utils
label_desc: Use image node_id 2 for calling the SAGENet obj's underlying member method code with example usage: obj.forward(blocks, h) and returns: h