class_docstr: Pipeline-like chaining a fitted classifier and its fitted calibrators.
  
  Parameters
  ----------
  estimator : estimator instance
      Fitted classifier.
  
  calibrators : list of fitted estimator instances
      List of fitted calibrators (either 'IsotonicRegression' or
      '_SigmoidCalibration'). The number of calibrators equals the number of
      classes. However, if there are 2 classes, the list contains only one
      fitted calibrator.
  
  classes : array-like of shape (n_classes,)
      All the prediction classes.
  
  method : {'sigmoid', 'isotonic'}, default='sigmoid'
      The method to use for calibration. Can be 'sigmoid' which
      corresponds to Platt's method or 'isotonic' which is a
      non-parametric approach based on isotonic regression.
code: def __init__(
      self, estimator, calibrators, *, classes, method="sigmoid"
  ):
      self.estimator = estimator
      self.calibrators = calibrators
      self.classes = classes
      self.method = method
  
imports: import warnings
  from functools import partial
  from inspect import signature
  from math import log
  from numbers import Integral, Real
  import numpy
  from scipy.optimize import fmin_bfgs
  from scipy.special import expit, xlogy
  from sklearn.utils import Bunch
  from .base import BaseEstimator, ClassifierMixin, MetaEstimatorMixin, RegressorMixin, _fit_context, clone
  from .isotonic import IsotonicRegression
  from .model_selection import check_cv, cross_val_predict
  from .preprocessing import LabelEncoder, label_binarize
  from .svm import LinearSVC
  from .utils import _safe_indexing, column_or_1d, indexable
  from .utils._param_validation import HasMethods, Hidden, Interval, StrOptions, validate_params
  from .utils._plotting import _BinaryClassifierCurveDisplayMixin
  from .utils.metadata_routing import MetadataRouter, MethodMapping, _routing_enabled, process_routing
  from .utils.multiclass import check_classification_targets
  from .utils.parallel import Parallel, delayed
  from .utils.validation import _check_fit_params, _check_pos_label_consistency, _check_sample_weight, _num_samples, check_consistent_length, check_is_fitted
label_desc: Use image node_id 1 to create a new _CalibratedClassifier object  with example: obj = _CalibratedClassifier(estimator, calibrators)