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 time
  import dgl.function
  import dgl.nn
  import numpy
  import sklearn.linear_model
  import sklearn.metrics
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import tqdm
  from dgl.data import AsNodePredDataset, RedditDataset
  from dgl.dataloading import as_edge_prediction_sampler, 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