code: def forward(self, blocks, feats, norm=None):
      if blocks is None:
          # full graph training
          blocks = [self.g] * len(self.layers)
      h = feats
      for layer, block in zip(self.layers, blocks):
          block = block.to(self.device)
          h = layer(block, h, block.edata["etype"], block.edata["norm"])
      return h
  
imports: import argparse
  import gc
  import logging
  import time
  from pathlib import Path
  from types import SimpleNamespace
  import dgl
  import numpy
  import torch
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  from dgl.nn import RelGraphConv
  from torch.multiprocessing import Queue
  from torch.nn.parallel import DistributedDataParallel
  from torch.utils.data import DataLoader
  from ..None import utils
label_desc: Use image node_id 2 for calling the EntityClassify obj's underlying member method code with example usage: obj.forward(blocks, feats, norm) and returns: h