code: def forward(self, blocks, x):
      h = x
      for l, (layer, block) in enumerate(zip(self.layers, blocks)):
          h = layer(block, h)
          if l < len(self.layers) - 1:
              h = h.flatten(1)
      h = h.mean(1)
      return h.log_softmax(dim=-1)
  
file_path: dgl/benchmarks/benchmarks/model_speed/bench_gat_ns.py
imports: import time
  import traceback
  import dgl
  import dgl.nn.pytorch
  import torch
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torch.utils.data import DataLoader
  from ..None import utils
label_desc: Use image node_id 2 for calling the GAT obj's underlying member method code with example usage: obj.forward(blocks, x) and returns: h