code: def __init__(
      self, hidden_channels, out_channels, num_heads, num_layers
  ):
      super().__init__()
  
      self.lin_dict = torch.nn.ModuleDict()
      for node_type in data.node_types:
          self.lin_dict[node_type] = Linear(-1, hidden_channels)
  
      self.convs = torch.nn.ModuleList()
      for _ in range(num_layers):
          conv = HGTConv(
              hidden_channels,
              hidden_channels,
              data.metadata(),
              num_heads,
              group="sum",
          )
          self.convs.append(conv)
  
      self.lin = Linear(hidden_channels, out_channels)
  
file_path: pytorch_geometric/examples/hetero/hgt_dblp.py
imports: import os.path
  import torch
  import torch.nn.functional
  import torch_geometric.transforms
  from torch_geometric.datasets import DBLP
  from torch_geometric.nn import HGTConv, Linear
label_desc: Use image node_id 1 to create a new HGT object from inherited base classes: torch.nn with example: obj = HGT(hidden_channels, out_channels, num_heads, num_layers)