code: def __init__(
      self,
      in_channels,
      hidden_channels,
      out_channels,
      num_layers,
      dropout,
      num_groups=2,
  ):
      super().__init__()
  
      self.dropout = dropout
  
      self.lin1 = Linear(in_channels, hidden_channels)
      self.lin2 = Linear(hidden_channels, out_channels)
      self.norm = LayerNorm(hidden_channels, elementwise_affine=True)
  
      assert hidden_channels % num_groups == 0
      self.convs = torch.nn.ModuleList()
      for _ in range(num_layers):
          conv = GNNBlock(
              hidden_channels // num_groups,
              hidden_channels // num_groups,
          )
          self.convs.append(GroupAddRev(conv, num_groups=num_groups))
  
imports: import os.path
  import time
  import torch
  import torch.nn.functional
  from ogb.nodeproppred import Evaluator, PygNodePropPredDataset
  from torch.nn import LayerNorm, Linear
  from tqdm import tqdm
  import torch_geometric.transforms
  from torch_geometric.loader import RandomNodeLoader
  from torch_geometric.nn import GroupAddRev, SAGEConv
  from torch_geometric.utils import index_to_mask
label_desc: Use image node_id 1 to create a new RevGNN object from inherited base classes: torch.nn with example: obj = RevGNN(in_channels, hidden_channels, out_channels, num_layers, dropout, num_groups)