code: def __init__(
      self,
      num_metapath,
      in_size,
      hidden_size,
      out_size,
      num_heads,
      dropout,
  ):
      super(HAN, self).__init__()
  
      self.layers = nn.ModuleList()
      self.layers.append(
          HANLayer(
              num_metapath, in_size, hidden_size, num_heads[0], dropout
          )
      )
      for l in range(1, len(num_heads)):
          self.layers.append(
              HANLayer(
                  num_metapath,
                  hidden_size * num_heads[l - 1],
                  hidden_size,
                  num_heads[l],
                  dropout,
              )
          )
      self.predict = nn.Linear(hidden_size * num_heads[-1], out_size)
  
imports: import dgl
  import numpy
  import argparse
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn.pytorch import GATConv
  from dgl.sampling import RandomWalkNeighborSampler
  from sklearn.metrics import f1_score
  from torch.utils.data import DataLoader
  from model_hetero import SemanticAttention
  from utils import EarlyStopping, set_random_seed
label_desc: Use image node_id 1 to create a new HAN object from inherited base classes: nn with example: obj = HAN(num_metapath, in_size, hidden_size, out_size, num_heads, dropout)