class_docstr: Implementation of frequency adaptive graph convolution networks (FAGCN)
class_docstr_tok: ["Implementation","of","frequency","adaptive","graph","convolution","networks","(","FAGCN",")"]
code: def __init__(
      self,
      input_size,
      hidden_size,
      num_class,
      drop=0.6,
      eps=0.2,
      num_layer=3,
      **kwargs,
  ):
      super(FAGCN, self).__init__()
  
      self.input_size = input_size
      self.hidden_size = hidden_size
      self.num_class = num_class
      self.dropout = nn.Dropout(p=drop)
      self.eps = eps
      self.num_layer = num_layer
  
      self.gnn_layer = nn.LayerList()
      for _ in range(self.num_layer):
          self.gnn_layer.append(pgl.nn.FAConv(self.hidden_size, drop))
  
      self.t1 = nn.Linear(self.input_size, self.hidden_size)
      self.t2 = nn.Linear(self.hidden_size, self.num_class)
  
imports: import pgl
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 1 to create a new FAGCN object from inherited base classes: nn with example: obj = FAGCN(input_size, hidden_size, num_class, drop, eps, num_layer)