code: def forward(self, x_i, x_j):
      x = x_i * x_j
      for lin in self.lins[:-1]:
          x = lin(x)
          x = F.relu(x)
          x = F.dropout(x, p=self.dropout, training=self.training)
      x = self.lins[-1](x)
      return torch.sigmoid(x)
  
imports: import argparse
  import math
  import dgl
  import torch
  import torch.nn.functional
  from dgl.dataloading.negative_sampler import GlobalUniform
  from dgl.nn.pytorch import GraphConv, SAGEConv
  from ogb.linkproppred import DglLinkPropPredDataset, Evaluator
  from torch.nn import Linear
  from torch.utils.data import DataLoader
label_desc: Use image node_id 3 for calling the LinkPredictor obj's underlying member method code with example usage: obj.forward(x_i, x_j) and returns: torch