code: def __init__(
      self,
      in_channels,
      hidden_channels,
      out_channels,
      num_layers,
      dropout,
  ):
      super(LinkPredictor, self).__init__()
  
      self.lins = torch.nn.ModuleList()
      self.lins.append(Linear(in_channels, hidden_channels))
      for _ in range(num_layers - 2):
          self.lins.append(Linear(hidden_channels, hidden_channels))
      self.lins.append(Linear(hidden_channels, out_channels))
  
      self.dropout = dropout
      self.reset_parameters()
  
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 1 to create a new LinkPredictor object from inherited base classes: torch.nn with example: obj = LinkPredictor(in_channels, hidden_channels, out_channels, num_layers, dropout)