code: def forward(self, x: Tensor, edge_index: Tensor) -> Tensor:
      for i, (conv, skip) in enumerate(zip(self.convs, self.skips)):
          x = conv(x, edge_index) + skip(x)
          if i != self.num_layers - 1:
              x = F.elu(x)
              x = F.dropout(x, p=0.5, training=self.training)
      return x
  
imports: import copy
  import os
  import os.path
  from typing import Any, Tuple, Union
  import intel_extension_for_pytorch
  import oneccl_bindings_for_pytorch
  import torch
  import torch.distributed
  import torch.nn.functional
  from ogb.nodeproppred import Evaluator, PygNodePropPredDataset
  from torch import Tensor
  from torch.nn import Linear
  from torch.nn.parallel import DistributedDataParallel
  from tqdm import tqdm
  from torch_geometric.loader import NeighborLoader
  from torch_geometric.nn import GATConv
  from torch_geometric.profile import get_stats_summary, profileit
label_desc: Use image node_id 2 for calling the GAT obj's underlying member method code with example usage: obj.forward(x, edge_index) and returns: x