code: def __init__(self, in_channels, hidden_channels, out_channels, heads):
      super().__init__()
      self.conv1 = GATConv(
          in_channels, hidden_channels, heads, dropout=0.6
      )
      # On the Pubmed dataset, use `heads` output heads in `conv2`.
      self.conv2 = GATConv(
          hidden_channels * heads,
          out_channels,
          heads=1,
          concat=False,
          dropout=0.6,
      )
  
file_path: pytorch_geometric/examples/gat.py
imports: import argparse
  import os.path
  import time
  import torch
  import torch.nn.functional
  import torch_geometric.transforms
  from torch_geometric.datasets import Planetoid
  from torch_geometric.logging import init_wandb, log
  from torch_geometric.nn import GATConv
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, heads)