class_docstr: Sigmoid regression model.
  
  Attributes
  ----------
  a_ : float
      The slope.
  
  b_ : float
      The intercept.
docstr: Fit the model using X, y as training data.
  
  Parameters
  ----------
  X : array-like of shape (n_samples,)
      Training data.
  
  y : array-like of shape (n_samples,)
      Training target.
  
  sample_weight : array-like of shape (n_samples,), default=None
      Sample weights. If None, then samples are equally weighted.
  
  Returns
  -------
  self : object
      Returns an instance of self.
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code: def fit(self, X, y, sample_weight=None):
      """Fit the model using X, y as training data.
  
      Parameters
      ----------
      X : array-like of shape (n_samples,)
          Training data.
  
      y : array-like of shape (n_samples,)
          Training target.
  
      sample_weight : array-like of shape (n_samples,), default=None
          Sample weights. If None, then samples are equally weighted.
  
      Returns
      -------
      self : object
          Returns an instance of self.
      """
      X = column_or_1d(X)
      y = column_or_1d(y)
      X, y = indexable(X, y)
  
      self.a_, self.b_ = _sigmoid_calibration(X, y, sample_weight)
      return self
  
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 for calling the _SigmoidCalibration obj's underlying member method code with example usage: obj.fit(X, y, sample_weight) and returns: self