class_docstr: AutoHeterGNN
class_docstr_tok: ["AutoHeterGNN"]
code: def __init__(
      self,
      hidden_size,
      layer_type,
      num_layers,
      etype_len,
      act=None,
      alpha_residual=0.9,
      use_degree_norm=False,
      interact_mode="sum",
      return_weight=False,
  ):
      super(AutoHeterGNN, self).__init__()
      self.etype_len = etype_len
      self.num_layers = num_layers
      self.hidden_size = hidden_size
      self.layer_type = layer_type
      if self.layer_type == "Gatne":
          interact_mode = "gatne"
      self.alpha_residual = alpha_residual
      self.use_degree_norm = use_degree_norm
      self.return_weight = return_weight
      sub_layer_dict = OrderedDict()
      shared_sub_pre_layer = []
  
      for i in range(self.num_layers):
          if i == self.num_layers - 1:
              cur_act = None
          else:
              cur_act = act
          shared_sub_pre_layer.append(
              getattr(layers, "Pre" + layer_type, Identity)(
                  hidden_size, act, use_degree_norm=use_degree_norm
              )
          )
          for j in range(self.etype_len):
              sub_layer_dict[(i, j)] = getattr(layers, layer_type)(
                  hidden_size, act, use_degree_norm=use_degree_norm
              )
          sub_layer_dict[(i, self.etype_len)] = FeatureInteraction(
              interact_mode, hidden_size, self.etype_len
          )
  
      self.shared_sub_pre_layer = nn.LayerList(shared_sub_pre_layer)
      self.rgnn_dict = nn.LayerDict(sub_layer_dict)
      self.hcl_buffer = []
  
file_path: PGL/apps/PGLBox/src/models/auto_heter_gnn.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from collections import OrderedDict
  import paddle.static
  import pgl
  from .None import model_util
  from .None import layers
label_desc: Use image node_id 1 to create a new AutoHeterGNN object from inherited base classes: nn with example: obj = AutoHeterGNN(hidden_size, layer_type, num_layers, etype_len, act, alpha_residual, use_degree_norm, interact_mode, return_weight)