code: def forward(self, graph, feature):
      neigh_feature = graph.send_recv(feature, reduce_func="sum")
      output = neigh_feature + feature * (self.epsilon + 1.0)
      output = self.linear1(output)
  
      if self.activation is not None:
          output = self.activation(output)
  
      output = self.linear2(output)
  
      return output
  
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 GINConv obj's underlying member method code with example usage: obj.forward(graph, feature) and returns: output