code: def loss(self, preds, y, indices):
      loss = 0
      loss_func = F.cross_entropy
      for node_type in preds:
          idx = indices[node_type]
          loss += loss_func(preds[node_type][idx], y[node_type][idx])
      return loss
  
imports: import copy
  import torch
  import argparse
  import deepsnap
  import numpy
  import torch.nn
  import torch.nn.functional
  import torch_geometric.nn
  from sklearn.metrics import f1_score
  from deepsnap.hetero_gnn import forward_op, HeteroSAGEConv
  from deepsnap.hetero_graph import HeteroGraph
  from torch_sparse import SparseTensor, matmul
label_desc: Use image node_id 3 for calling the HeteroGNN obj's underlying member method code with example usage: obj.loss(preds, y, indices) and returns: loss