class_docstr: Base class for inductive conformal predictors.
      
class_docstr_tok: ["Base","class","for","inductive","conformal","predictors","."]
code: def _update_calibration_set(
      self, x: np.array, y: np.array, increment: bool
  ) -> None:
      if (
          increment
          and self.cal_x is not None
          and self.cal_y is not None
      ):
          self.cal_x = np.vstack([self.cal_x, x])
          self.cal_y = np.hstack([self.cal_y, y])
      else:
          self.cal_x, self.cal_y = x, y
  
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 BaseIcp obj's underlying member method code with example usage: obj._update_calibration_set(x, y, increment) without return types