code: def loss(self, scores, labels):
      is_labeled = labels == labels
      loss = nn.BCEWithLogitsLoss()(
          scores[is_labeled], labels[is_labeled].float()
      )
      return loss
  
imports: import argparse
  import random
  import dgl
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from dgl.dataloading import GraphDataLoader
  from ogb.graphproppred import Evaluator
  from ogb.graphproppred.mol_encoder import AtomEncoder
  from preprocessing import prepare_dataset
  from torch.utils.data import Dataset
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling the DGNNet obj's underlying member method code with example usage: obj.loss(scores, labels) and returns: loss