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(torch.nn.Linear(in_channels, hidden_channels))
      for _ in range(num_layers - 2):
          self.lins.append(
              torch.nn.Linear(hidden_channels, hidden_channels)
          )
      self.lins.append(torch.nn.Linear(hidden_channels, out_channels))
  
      self.dropout = dropout
  
imports: import argparse
  import torch
  import numpy
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from torch_sparse import SparseTensor
  from torch_geometric.data import ClusterData, ClusterLoader
  from torch_geometric.nn import GCNConv
  from torch_geometric.utils import to_undirected
  from ogb.linkproppred import PygLinkPropPredDataset, Evaluator
  from logger import Logger
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)