code: def forward(self, pair_graph, neg_pair_graph, blocks, x):
      hidden_x = x
      for layer_idx, (layer, block) in enumerate(
          zip(self.layers, blocks)
      ):
          hidden_x = layer(block, hidden_x)
          is_last_layer = layer_idx == len(self.layers) - 1
          if not is_last_layer:
              hidden_x = F.relu(hidden_x)
      pos_src, pos_dst = pair_graph.edges()
      neg_src, neg_dst = neg_pair_graph.edges()
      hidden_pos = self.predictor(hidden_x[pos_src] * hidden_x[pos_dst])
      hidden_neg = self.predictor(hidden_x[neg_src] * hidden_x[neg_dst])
      return hidden_pos, hidden_neg
  
imports: import argparse
  import time
  import dgl
  import dgl.nn
  import torch
  import torch.nn
  import torch.nn.functional
  import tqdm
  from dgl.dataloading import as_edge_prediction_sampler, DataLoader, MultiLayerFullNeighborSampler, negative_sampler, NeighborSampler
  from ogb.linkproppred import DglLinkPropPredDataset, Evaluator
label_desc: Use image node_id 2 for calling the SAGE obj's underlying member method code with example usage: obj.forward(pair_graph, neg_pair_graph, blocks, x) and returns: hidden_pos,  hidden_neg