code: def __init__(
      self,
      in_channels,
      hidden_channels,
      out_channels,
      num_layers,
      dropout,
  ):
      super(GCN, self).__init__()
  
      self.convs = torch.nn.ModuleList()
      self.convs.append(
          GCNConv(in_channels, hidden_channels, normalize=False)
      )
      for _ in range(num_layers - 2):
          self.convs.append(
              GCNConv(hidden_channels, hidden_channels, normalize=False)
          )
      self.convs.append(
          GCNConv(hidden_channels, out_channels, normalize=False)
      )
  
      self.dropout = dropout
  
file_path: ogb/examples/nodeproppred/proteins/gnn.py
imports: import argparse
  import torch
  import torch.nn.functional
  import torch_geometric.transforms
  from torch_geometric.nn import GCNConv, SAGEConv
  from ogb.nodeproppred import PygNodePropPredDataset, Evaluator
  from logger import Logger
label_desc: Use image node_id 1 to create a new GCN object from inherited base classes: torch.nn with example: obj = GCN(in_channels, hidden_channels, out_channels, num_layers, dropout)