code: def __init__(
      self,
      in_channels: int,
      hidden_channels: int,
      out_channels: int,
      num_layers: int,
      heads: int,
  ):
      super().__init__()
  
      self.num_layers = num_layers
  
      self.convs = torch.nn.ModuleList()
      self.convs.append(
          GATConv(dataset.num_features, hidden_channels, heads)
      )
      for _ in range(num_layers - 2):
          self.convs.append(
              GATConv(heads * hidden_channels, hidden_channels, heads)
          )
      self.convs.append(
          GATConv(
              heads * hidden_channels, out_channels, heads, concat=False
          )
      )
  
      self.skips = torch.nn.ModuleList()
      self.skips.append(
          Lin(dataset.num_features, hidden_channels * heads)
      )
      for _ in range(num_layers - 2):
          self.skips.append(
              Lin(hidden_channels * heads, hidden_channels * heads)
          )
      self.skips.append(Lin(hidden_channels * heads, out_channels))
  
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 1 to create a new GAT object from inherited base classes: torch.nn with example: obj = GAT(in_channels, hidden_channels, out_channels, num_layers, heads)