code: def forward(self, blocks, x):
      h = x
      for l, (layer, block) in enumerate(zip(self.layers, blocks)):
          h = layer(block, h)
          if l != len(self.layers) - 1:
              h = F.relu(h)
              h = self.dropout(h)
      return h
  
imports: import argparse
  import os
  import dgl.nn
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
  import tqdm
  from dgl.data import AsNodePredDataset
  from dgl.dataloading import DataLoader, MultiLayerFullNeighborSampler, NeighborSampler
  from dgl.multiprocessing import shared_tensor
  from ogb.nodeproppred import DglNodePropPredDataset
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 2 for calling the SAGE obj's underlying member method code with example usage: obj.forward(blocks, x) and returns: h