code: def __init__(
      self,
      num_meta_paths,
      in_size,
      out_size,
      hidden_size=8,
      num_heads=8,
      dropout=0.6,
  ):
      super().__init__()
  
      self.gat_layers = nn.ModuleList()
      for _ in range(num_meta_paths):
          self.gat_layers.append(
              GATConv(in_size, hidden_size, num_heads, dropout)
          )
  
      in_size = hidden_size * num_heads
      self.semantic_attention = SemanticAttention(in_size)
      self.predict = nn.Linear(in_size, out_size)
  
imports: import pickle
  import dgl.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.data.utils import _get_dgl_url, download, get_download_dir
  from torch.optim import Adam
label_desc: Use image node_id 1 to create a new HAN object from inherited base classes: nn with example: obj = HAN(num_meta_paths, in_size, out_size, hidden_size, num_heads, dropout)