code: def forward(self, data):
      x = forward_op(data.node_feature, self.dropouts1)
      x = forward_op(x, self.relus1)
      x = self.conv1(x, data.edge_index)
      x = forward_op(x, self.dropouts2)
      x = forward_op(x, self.relus2)
      x = self.conv2(x, data.edge_index)
      return x
  
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 2 for calling the HeteroNet obj's underlying member method code with example usage: obj.forward(data) and returns: x