code: def __init__(self, in_channels, out_channels):
      super().__init__()
      self.norm = LayerNorm(in_channels, elementwise_affine=True)
      self.conv = SAGEConv(in_channels, out_channels)
  
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 GNNBlock object from inherited base classes: torch.nn with example: obj = GNNBlock(in_channels, out_channels)