code: def __init__(
      self,
      in_channels: Union[int, Dict[str, int]],
      out_channels: int,
      hidden_channels=128,
      heads=8,
  ):
      super().__init__()
      self.han_conv = HANConv(
          in_channels,
          hidden_channels,
          heads=heads,
          dropout=0.6,
          metadata=data.metadata(),
      )
      self.lin = nn.Linear(hidden_channels, out_channels)
  
file_path: pytorch_geometric/examples/hetero/han_imdb.py
imports: import os.path
  from typing import Dict, List, Union
  import torch
  import torch.nn.functional
  from torch import nn
  import torch_geometric.transforms
  from torch_geometric.datasets import IMDB
  from torch_geometric.nn import HANConv
label_desc: Use image node_id 1 to create a new HAN object from inherited base classes: nn with example: obj = HAN(in_channels, out_channels, hidden_channels, heads)