code: def loss(self, pred, y, node_label_index):
      loss = loss_op(pred, y, node_label_index, F.cross_entropy)
      return loss
  
file_path: deepsnap/examples/node_classification_hetero/node_classification_syn_tensor.py
imports: import copy
  import torch
  import torch.nn
  import torch.nn.functional
  from utils import generate_convs, concatenate_citeseer_cora
  from deepsnap.hetero_gnn import HeteroConv, HeteroSAGEConv, forward_op, loss_op
  from torch_geometric.datasets import Planetoid
  from deepsnap.dataset import GraphDataset
  from deepsnap.batch import Batch
  from deepsnap.hetero_graph import HeteroGraph
  from torch.utils.data import DataLoader
label_desc: Use image node_id 3 for calling the HeteroNet obj's underlying member method code with example usage: obj.loss(pred, y, node_label_index) and returns: loss