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 forward(self, gs, h):
      semantic_embeddings = []
  
      for i, g in enumerate(gs):
          semantic_embeddings.append(
              self.gat_layers[i](g, h).flatten(1)
          )
      semantic_embeddings = torch.stack(
          semantic_embeddings, dim=1
      )  # (N, M, D * K)
  
      return self.semantic_attention(semantic_embeddings)
  
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 2 for calling the HANLayer obj's underlying member method code with example usage: obj.forward(gs, h) and returns: self