code: def __init__(
      self,
      in_feats,
      num_heads,
      n_hidden,
      n_classes,
      n_layers,
      activation,
      dropout=0.0,
  ):
      super().__init__()
      self.n_layers = n_layers
      self.n_hidden = n_hidden
      self.n_classes = n_classes
      self.layers = nn.ModuleList()
      self.num_heads = num_heads
      self.layers.append(
          dglnn.GATConv(
              in_feats,
              n_hidden,
              num_heads=num_heads,
              feat_drop=dropout,
              attn_drop=dropout,
              activation=activation,
              negative_slope=0.2,
          )
      )
      for i in range(1, n_layers - 1):
          self.layers.append(
              dglnn.GATConv(
                  n_hidden * num_heads,
                  n_hidden,
                  num_heads=num_heads,
                  feat_drop=dropout,
                  attn_drop=dropout,
                  activation=activation,
                  negative_slope=0.2,
              )
          )
      self.layers.append(
          dglnn.GATConv(
              n_hidden * num_heads,
              n_classes,
              num_heads=num_heads,
              feat_drop=dropout,
              attn_drop=dropout,
              activation=None,
              negative_slope=0.2,
          )
      )
  
imports: import argparse
  import time
  from functools import partial
  import dgl
  import dgl.nn.pytorch
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import tqdm
  from ogb.nodeproppred import DglNodePropPredDataset
  from sampler import ClusterIter, subgraph_collate_fn
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 to create a new GAT object from inherited base classes: nn with example: obj = GAT(in_feats, num_heads, n_hidden, n_classes, n_layers, activation, dropout)