code: def forward(self, g, h):
      semantic_embeddings = []
      # obtain metapath reachable graph
      if self._cached_graph is None or self._cached_graph is not g:
          self._cached_graph = g
          self._cached_coalesced_graph.clear()
          for meta_path_pattern in self.meta_path_patterns:
              self._cached_coalesced_graph[
                  meta_path_pattern
              ] = dgl.metapath_reachable_graph(g, meta_path_pattern)
  
      for i, meta_path_pattern in enumerate(self.meta_path_patterns):
          new_g = self._cached_coalesced_graph[meta_path_pattern]
          semantic_embeddings.append(
              self.gat_layers[i](new_g, h).flatten(1)
          )
      semantic_embeddings = torch.stack(
          semantic_embeddings, dim=1
      )  # (N, M, D * K)
  
      return self.semantic_attention(semantic_embeddings)
  
imports: import dgl
  import dgl.function
  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(g, h) and returns: self