class_docstr: Inductive conformal classifier.
  
  Parameters
  ----------
  nc_function : BaseScorer
      Nonconformity scorer object used to calculate nonconformity of
      calibration examples and test patterns. Should implement ``fit(x, y)``
      and ``calc_nc(x, y)``.
  
  smoothing : boolean
      Decides whether to use stochastic smoothing of p-values.
  
  Attributes
  ----------
  cal_x : numpy array of shape [n_cal_examples, n_features]
      Inputs of calibration set.
  
  cal_y : numpy array of shape [n_cal_examples]
      Outputs of calibration set.
  
  nc_function : BaseScorer
      Nonconformity scorer object used to calculate nonconformity scores.
  
  classes : numpy array of shape [n_classes]
      List of class labels, with indices corresponding to output columns
       of IcpClassifier.predict()
  
  See also
  --------
  IcpRegressor
  
  References
  ----------
  .. [1] Papadopoulos, H., & Haralambous, H. (2011). Reliable prediction
      intervals with regression neural networks. Neural Networks, 24(8),
      842-851.
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docstr: Predict the output values for a set of input patterns, using
  the confidence-and-credibility output scheme.
  
  Parameters
  ----------
  x : numpy array of shape [n_samples, n_features]
      Inputs of patters for which to predict output values.
  
  Returns
  -------
  p : numpy array of shape [n_samples, 3]
      p contains three columns: the first column contains the most
      likely class for each test pattern; the second column contains
      the confidence in the predicted class label, and the third column
      contains the credibility of the prediction.
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code: def predict_conf(self, x):
      """Predict the output values for a set of input patterns, using
      the confidence-and-credibility output scheme.
  
      Parameters
      ----------
      x : numpy array of shape [n_samples, n_features]
          Inputs of patters for which to predict output values.
  
      Returns
      -------
      p : numpy array of shape [n_samples, 3]
          p contains three columns: the first column contains the most
          likely class for each test pattern; the second column contains
          the confidence in the predicted class label, and the third column
          contains the credibility of the prediction.
      """
      p = self.predict(x, significance=None)
      label = p.argmax(axis=1)
      credibility = p.max(axis=1)
      for i, idx in enumerate(label):
          p[i, idx] = -np.inf
      confidence = 1 - p.max(axis=1)
  
      return np.array([label, confidence, credibility]).T
  
imports: from collections import defaultdict
  from functools import partial
  from typing import Optional, Union
  import numpy
  from sklearn.base import BaseEstimator
  from lightwood.analysis.nc.base import RegressorMixin, ClassifierMixin, TSMixin
  from types import FunctionType
label_desc: Use image node_id 5 for calling the IcpClassifier obj's underlying member method code with example usage: obj.predict_conf(x) and returns: np