code: def __init__(
      self,
      out_size,
      hidden_size=80,
      pos_enc_size=2,
      num_layers=8,
      num_heads=8,
  ):
      super().__init__()
      self.atom_encoder = AtomEncoder(hidden_size)
      self.pos_linear = nn.Linear(pos_enc_size, hidden_size)
      self.layers = nn.ModuleList(
          [GTLayer(hidden_size, num_heads) for _ in range(num_layers)]
      )
      self.pooler = dglnn.SumPooling()
      self.predictor = nn.Sequential(
          nn.Linear(hidden_size, hidden_size // 2),
          nn.ReLU(),
          nn.Linear(hidden_size // 2, hidden_size // 4),
          nn.ReLU(),
          nn.Linear(hidden_size // 4, out_size),
      )
  
imports: import dgl
  import dgl.nn
  import dgl.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from dgl.data import AsGraphPredDataset
  from dgl.dataloading import GraphDataLoader
  from ogb.graphproppred import collate_dgl, DglGraphPropPredDataset, Evaluator
  from ogb.graphproppred.mol_encoder import AtomEncoder
  from tqdm import tqdm
label_desc: Use image node_id 1 to create a new GTModel object from inherited base classes: nn with example: obj = GTModel(out_size, hidden_size, pos_enc_size, num_layers, num_heads)