class_docstr: Meta-estimator to set a custom probability threshold.
code: def fit(self, X, y, **fit_params):
      if (
          not isinstance(self.probability_threshold, (int, float))
          or self.probability_threshold > 1
          or self.probability_threshold < 0
      ):
          raise TypeError(
              "probability_threshold parameter only accepts value between 0 to 1."
          )
  
      if self.classifier is None:
          from sklearn.linear_model import LogisticRegression
  
          self.classifier_ = LogisticRegression()
      else:
          self.classifier_ = clone(self.classifier)
  
      self.classifier_.fit(X, y, **fit_params)
      return self
  
imports: import sklearn.compose
  from sklearn.base import BaseEstimator, ClassifierMixin, clone
  from sklearn.compose import TransformedTargetRegressor
  from sklearn.preprocessing import PowerTransformer
  from sklearn.utils.validation import check_is_fitted
label_desc: Use image node_id 2 for calling the CustomProbabilityThresholdClassifier obj's underlying member method code with example usage: obj.fit(X, y) and returns: self