code: def pred_conn(self, edges):
      src_feat = self.src_mlp(edges.src["conv_features"])
      dst_feat = self.dst_mlp(edges.dst["conv_features"])
      pred_conn = self.classifier_conn(src_feat + dst_feat)
      return {"pred_conn": pred_conn}
  
file_path: dgl/examples/pytorch/hilander/models/lander.py
imports: import dgl
  import dgl.function
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from .focal_loss import FocalLoss
  from .graphconv import GraphConv
label_desc: Use image node_id 2 for calling the LANDER obj's underlying member method code with example usage: obj.pred_conn(edges) and returns: dict