code: def true_positive_rate(self, idx):
      nom = self.true_positives(idx)
      den = self.sum_conditions[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 7 for calling the ConfusionMatrix obj's underlying member method code with example usage: obj.true_positive_rate(idx) and returns: int, unknown