code: def forward(self, node_feature, edge_index):
      x = node_feature
      x = self.convs1(x, edge_index)
      x = forward_op(x, self.bns1)
      x = forward_op(x, self.relus1)
      x = self.convs2(x, edge_index)
      x = forward_op(x, self.bns2)
      x = forward_op(x, self.relus2)
      x = forward_op(x, self.post_mps)
      return x
  
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 2 for calling the HeteroGNN obj's underlying member method code with example usage: obj.forward(node_feature, edge_index) and returns: x