class_docstr: compute the confidence for edges
class_docstr_tok: ["compute","the","confidence","for","edges"]
code: def forward(self, edges):
      score_pred = nd.log_softmax(edges.data["preds"])[:, 1:].max(
          axis=1
      )
      score_phr = (
          score_pred
          + edges.src["node_class_logit"]
          + edges.dst["node_class_logit"]
      )
      return {"score_pred": score_pred, "score_phr": score_phr}
  
imports: import pickle
  import dgl
  import gluoncv
  import mxnet
  import numpy
  from dgl.nn.mxnet import GraphConv
  from dgl.utils import toindex
  from mxnet import nd
  from mxnet.gluon import nn
label_desc: Use image node_id 2 for calling the EdgeConfMLP obj's underlying member method code with example usage: obj.forward(edges) and returns: dict