code: def forward(self, blocks):
      h = blocks[0].srcdata["features"]
      updates = []
      for layer, block in zip(self.layers, blocks):
          # We need to first copy the representation of nodes on the RHS from the
          # appropriate nodes on the LHS.
          # Note that the shape of h is (num_nodes_LHS, D) and the shape of h_dst
          # would be (num_nodes_RHS, D)
          h_dst = h[: block.number_of_dst_nodes()]
          hbar_src = block.srcdata["hist"]
          agg_hbar_dst = block.dstdata["agg_hist"]
          # Then we compute the updated representation on the RHS.
          # The shape of h now becomes (num_nodes_RHS, D)
          h = layer(block, (h, h_dst), (hbar_src, agg_hbar_dst))
          block.dstdata["h_new"] = h
      return h
  
imports: import argparse
  import time
  import dgl
  import dgl.function
  import dgl.nn.pytorch
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import tqdm
  from dgl.data import RedditDataset
  from torch.utils.data import DataLoader
label_desc: Use image node_id 2 for calling the SAGE obj's underlying member method code with example usage: obj.forward(blocks) and returns: h