code: def forward(self, g, h):
      for gnn in self.layers:
          h = gnn(g, h)
  
      return self.predict(h)
  
imports: import argparse
  import dgl
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn.pytorch import GATConv
  from dgl.sampling import RandomWalkNeighborSampler
  from model_hetero import SemanticAttention
  from sklearn.metrics import f1_score
  from torch.utils.data import DataLoader
  from utils import EarlyStopping, set_random_seed
label_desc: Use image node_id 2 for calling the HAN obj's underlying member method code with example usage: obj.forward(g, h) and returns: self