code: def forward(self, x, edge_index):
      x = self.lin1(x)
  
      # Generate a dropout mask which will be shared across GNN blocks:
      mask = None
      if self.training and self.dropout > 0:
          mask = torch.zeros_like(x).bernoulli_(1 - self.dropout)
          mask = mask.requires_grad_(False)
          mask = mask / (1 - self.dropout)
  
      for conv in self.convs:
          x = conv(x, edge_index, mask)
      x = self.norm(x).relu()
      x = F.dropout(x, p=self.dropout, training=self.training)
      return self.lin2(x)
  
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 3 for calling the RevGNN obj's underlying member method code with example usage: obj.forward(x, edge_index) and returns: self