class_docstr: HAN layer.
  
  Arguments
  ---------
  num_meta_paths : number of homogeneous graphs generated from the metapaths.
  in_size : input feature dimension
  out_size : output feature dimension
  layer_num_heads : number of attention heads
  dropout : Dropout probability
  
  Inputs
  ------
  g : list[DGLGraph]
      List of graphs
  h : tensor
      Input features
  
  Outputs
  -------
  tensor
      The output feature
code: def __init__(
      self, num_meta_paths, in_size, out_size, layer_num_heads, dropout
  ):
      super(HANLayer, self).__init__()
  
      # One GAT layer for each meta path based adjacency matrix
      self.gat_layers = nn.ModuleList()
      for i in range(num_meta_paths):
          self.gat_layers.append(
              GATConv(
                  in_size,
                  out_size,
                  layer_num_heads,
                  dropout,
                  dropout,
                  activation=F.elu,
              )
          )
      self.semantic_attention = SemanticAttention(
          in_size=out_size * layer_num_heads
      )
      self.num_meta_paths = num_meta_paths
  
file_path: AutoGL/test/performance/heterogeneous/dgl/han_base/model.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn.pytorch import GATConv
label_desc: Use image node_id 1 to create a new HANLayer object from inherited base classes: nn with example: obj = HANLayer(num_meta_paths, in_size, out_size, layer_num_heads, dropout)