code: def forward(self, x, edge_index, dropout_mask=None):
      x = self.norm(x).relu()
      if self.training and dropout_mask is not None:
          x = x * dropout_mask
      return self.conv(x, edge_index)
  
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 GNNBlock obj's underlying member method code with example usage: obj.forward(x, edge_index, dropout_mask) and returns: self