code: def forward(self, g, X, pos_enc):
      indices = torch.stack(g.edges())
      N = g.num_nodes()
      A = dglsp.spmatrix(indices, shape=(N, N))
      h = self.atom_encoder(X) + self.pos_linear(pos_enc)
      for layer in self.layers:
          h = layer(A, h)
      h = self.pooler(g, h)
  
      return self.predictor(h)
  
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 2 for calling the GTModel obj's underlying member method code with example usage: obj.forward(g, X, pos_enc) and returns: self