code: def __init__(
      self,
      in_channels,
      hidden_channels,
      out_channels,
      num_layers,
      dropout,
  ):
      super(SAGE, self).__init__()
  
      self.convs = torch.nn.ModuleList()
      self.convs.append(SAGEConv(in_channels, hidden_channels))
      for _ in range(num_layers - 2):
          self.convs.append(SAGEConv(hidden_channels, hidden_channels))
      self.convs.append(SAGEConv(hidden_channels, out_channels))
  
      self.dropout = dropout
  
imports: import argparse
  import torch
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from torch_geometric.utils import negative_sampling
  import torch_geometric.transforms
  from torch_geometric.nn import GCNConv, SAGEConv
  from ogb.linkproppred import PygLinkPropPredDataset, Evaluator
  from logger import Logger
label_desc: Use image node_id 1 to create a new SAGE object from inherited base classes: torch.nn with example: obj = SAGE(in_channels, hidden_channels, out_channels, num_layers, dropout)