class_docstr: Graph Transformer Layer
class_docstr_tok: ["Graph","Transformer","Layer"]
code: def __init__(self, hidden_size=80, num_heads=8):
      super().__init__()
      self.MHA = SparseMHA(hidden_size=hidden_size, num_heads=num_heads)
      self.batchnorm1 = nn.BatchNorm1d(hidden_size)
      self.batchnorm2 = nn.BatchNorm1d(hidden_size)
      self.FFN1 = nn.Linear(hidden_size, hidden_size * 2)
      self.FFN2 = nn.Linear(hidden_size * 2, hidden_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 GTLayer object from inherited base classes: nn with example: obj = GTLayer(hidden_size, num_heads)