code: def forward(self, block, H, HBar=None):
      if self.training:
          with block.local_scope():
              H_src, H_dst = H
              HBar_src, agg_HBar_dst = HBar
              block.dstdata["agg_hbar"] = agg_HBar_dst
              block.srcdata["hdelta"] = H_src - HBar_src
              block.update_all(
                  fn.copy_u("hdelta", "m"), fn.mean("m", "hdelta_new")
              )
              h_neigh = (
                  block.dstdata["agg_hbar"]
                  + block.dstdata["hdelta_new"]
              )
              h = self.W(th.cat([H_dst, h_neigh], 1))
              if self.activation is not None:
                  h = self.activation(h)
              return h
      else:
          with block.local_scope():
              H_src, H_dst = H
              block.srcdata["h"] = H_src
              block.update_all(
                  fn.copy_u("h", "m"), fn.mean("m", "h_new")
              )
              h_neigh = block.dstdata["h_new"]
              h = self.W(th.cat([H_dst, h_neigh], 1))
              if self.activation is not None:
                  h = self.activation(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 3 for calling the SAGEConvWithCV obj's underlying member method code with example usage: obj.forward(block, H, HBar) and returns: h, h