code: def __init__(
      self,
      meta_path_patterns,
      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(len(meta_path_patterns)):
          self.gat_layers.append(
              GATConv(
                  in_size,
                  out_size,
                  layer_num_heads,
                  dropout,
                  dropout,
                  activation=F.elu,
                  allow_zero_in_degree=True,
              )
          )
      self.semantic_attention = SemanticAttention(
          in_size=out_size * layer_num_heads
      )
      self.meta_path_patterns = list(
          tuple(meta_path_pattern)
          for meta_path_pattern in meta_path_patterns
      )
  
      self._cached_graph = None
      self._cached_coalesced_graph = {}
  
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 1 to create a new HANLayer object from inherited base classes: nn with example: obj = HANLayer(meta_path_patterns, in_size, out_size, layer_num_heads, dropout)