class_docstr: Implementation of frequency adaptive graph convolution networks (FAGCN)
class_docstr_tok: ["Implementation","of","frequency","adaptive","graph","convolution","networks","(","FAGCN",")"]
docstr: Args:
  
      graph: `pgl.Graph` instance
  
      feature: A tensor with shape (num_nodes, input_size)
  
  Return:
      A tensor with shape (num_nodes, output_size)
code: def forward(self, graph, feature):
      """
  
      Args:
  
          graph: `pgl.Graph` instance
  
          feature: A tensor with shape (num_nodes, input_size)
  
      Return:
          A tensor with shape (num_nodes, output_size)
  
      """
      feature = self.dropout(feature)
      feature = F.relu(self.t1(feature))
      feature = self.dropout(feature)
  
      h_0 = feature
      for i in range(len(self.gnn_layer)):
          feature = self.gnn_layer[i](graph, feature)
          feature = self.eps * h_0 + feature
  
      feature = self.t2(feature)
      return feature
  
imports: import pgl
  import paddle.nn
  import paddle.nn.functional
label_desc: Use image node_id 2 for calling the FAGCN obj's underlying member method code with example usage: obj.forward(graph, feature) and returns: feature