code: def negative_predictive_value(self, idx):
      nom = tn = self.true_negatives(idx)
      den = tn + self.false_negatives(idx)
      if den == 0 or den == np.nan:
          return 0
      else:
          return nom / den
  
imports: import logging
  from collections import OrderedDict
  import numpy
  from sklearn import metrics
  from sklearn.metrics import confusion_matrix
label_desc: Use image node_id 10 for calling the ConfusionMatrix obj's underlying member method code with example usage: obj.negative_predictive_value(idx) and returns: int, unknown