class_docstr: A standard multi-layer perceptron 
class_docstr_tok: ["A","standard","multi-layer","perceptron"]
code: def _accuracy(self):
      if self.categorical:
          self.accuracy = metrics.masked_accuracy(
              self.outputs,
              self.placeholders["labels"],
              self.placeholders["labels_mask"],
          )
  
imports: from collections import namedtuple
  import tensorflow
  import math
  import graphsage.layers
  import graphsage.metrics
  from .prediction import BipartiteEdgePredLayer
  from .aggregators import MeanAggregator, MaxPoolingAggregator, MeanPoolingAggregator, SeqAggregator, GCNAggregator
label_desc: Use image node_id 3 for calling the MLP obj's underlying member method code with example usage: obj._accuracy() without return types