class_docstr: make use of the pre-trained frequency prior
class_docstr_tok: ["make","use","of","the","pre-trained","frequency","prior"]
code: def forward(self, edges):
      ctx = edges.src["node_class_pred"].context
      src_ind = edges.src["node_class_pred"].asnumpy().astype(int)
      dst_ind = edges.dst["node_class_pred"].asnumpy().astype(int)
      prob = self.freq_prior[src_ind, dst_ind]
      out = nd.array(prob, ctx=ctx)
      return {"freq_prior": out}
  
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 EdgeFreqPrior obj's underlying member method code with example usage: obj.forward(edges) and returns: dict