class_docstr: One can also write a prediction function that predicts a vector for each edge with an MLP.
  Such vector can be used in further downstream tasks, e.g. as logits of a categorical distribution.
class_docstr_tok: ["One","can","also","write","a","prediction","function","that","predicts","a","vector","for","each","edge","with","an","MLP",".","Such","vector","can","be","used","in","further","downstream","tasks",",","e.g",".","as","logits","of","a","categorical","distribution","."]
code: def forward(self, graph, h):
      # h contains the node representations computed from the GNN defined
      # in the node classification section (Section 5.1).
      with graph.local_scope():
          graph.ndata["h"] = h
          graph.apply_edges(self.apply_edges)
          return graph.edata["score"]
  
imports: from typing import TYPE_CHECKING, Any
  from .None import constants
  import logging
label_desc: Use image node_id 3 for calling the MLPPredictor obj's underlying member method code with example usage: obj.forward(graph, h) and returns: graph