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 __init__(self, in_features, out_classes):
      super().__init__()
      nn, _, _, _, _, _, _ = lazy_import_networks()
      self.W = nn.Linear(in_features * 2, out_classes)
  
imports: from typing import TYPE_CHECKING, Any
  from .None import constants
  import logging
label_desc: Use image node_id 1 to create a new MLPPredictor object from inherited base classes: Module with example: obj = MLPPredictor(in_features, out_classes)