code: def class_stats(self, idx):
      return {
          "true_positives": self.true_positives(idx),
          "true_negatives": self.true_negatives(idx),
          "false_positives": self.false_positives(idx),
          "false_negatives": self.false_negatives(idx),
          "true_positive_rate": self.true_positive_rate(idx),
          "true_negative_rate": self.true_negative_rate(idx),
          "positive_predictive_value": self.positive_predictive_value(
              idx
          ),
          "negative_predictive_value": self.negative_predictive_value(
              idx
          ),
          "false_negative_rate": self.false_negative_rate(idx),
          "false_positive_rate": self.false_positive_rate(idx),
          "false_discovery_rate": self.false_discovery_rate(idx),
          "false_omission_rate": self.false_omission_rate(idx),
          "accuracy": self.accuracy(idx),
          "precision": self.precision(idx),
          "recall": self.recall(idx),
          "f1_score": self.f1_score(idx),
          "sensitivity": self.sensitivity(idx),
          "specificity": self.specificity(idx),
          "hit_rate": self.hit_rate(idx),
          "miss_rate": self.miss_rate(idx),
          "fall_out": self.fall_out(idx),
          "matthews_correlation_coefficient": self.matthews_correlation_coefficient(
              idx
          ),
          "informedness": self.informedness(idx),
          "markedness": self.markedness(idx),
      }
  
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 34 for calling the ConfusionMatrix obj's underlying member method code with example usage: obj.class_stats(idx) and returns: dict