class_docstr: Implementation of LightGCN
  
  This is an implementation of the paper LightGCN: Simplifying 
  and Powering Graph Convolution Network for Recommendation 
  (https://dl.acm.org/doi/10.1145/3397271.3401063).
class_docstr_tok: ["Implementation","of","LightGCN","This","is","an","implementation","of","the","paper","LightGCN",":","Simplifying","and","Powering","Graph","Convolution","Network","for","Recommendation","(","https",":","\/\/dl.acm.org\/doi\/10.1145\/3397271.3401063",")","."]
docstr: Args:
  
      graph: `pgl.Graph` instance.
  
      feature: A tensor with shape (num_nodes, input_size)
  
  Return:
  
      A tensor with shape (num_nodes, output_size)
docstr_tok: ["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 = graph.send_recv(feature, "sum")
      return feature
  
imports: import numpy
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import pgl
  from pgl.nn import functional
label_desc: Use image node_id 2 for calling the LightGCNConv obj's underlying member method code with example usage: obj.forward(graph, feature) and returns: feature