class_docstr: Meta-estimator to set a custom probability threshold.
code: def predict(self, X, **predict_params):
      check_is_fitted(self)
      if not hasattr(self.classifier_, "predict_proba"):
          return self.classifier_.predict(X, **predict_params)
      pred = self.classifier_.predict_proba(X, **predict_params)
      if pred.shape[1] > 2:
          raise ValueError(
              f"{self.__class__.__name__} can only be used for binary classification."
          )
      return (pred[:, 1] >= self.probability_threshold).astype("int")
  
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 3 for calling the CustomProbabilityThresholdClassifier obj's underlying member method code with example usage: obj.predict(X) and returns: unknown, self