class_docstr: Base class for inductive conformal predictors.
      
class_docstr_tok: ["Base","class","for","inductive","conformal","predictors","."]
docstr: Fit underlying nonconformity scorer.
  
  Parameters
  ----------
  x : numpy array of shape [n_samples, n_features]
      Inputs of examples for fitting the nonconformity scorer.
  
  y : numpy array of shape [n_samples]
      Outputs of examples for fitting the nonconformity scorer.
  
  Returns
  -------
  None
docstr_tok: ["Fit","underlying","nonconformity","scorer",".","Parameters","--","--","--","--","--","x",":","numpy","array","of","shape","[","n_samples",",","n_features","]","Inputs","of","examples","for","fitting","the","nonconformity","scorer",".","y",":","numpy","array","of","shape","[","n_samples","]","Outputs","of","examples","for","fitting","the","nonconformity","scorer",".","Returns","--","--","--","-","None"]
code: def fit(self, x: np.array, y: np.array) -> None:
      """Fit underlying nonconformity scorer.
  
      Parameters
      ----------
      x : numpy array of shape [n_samples, n_features]
          Inputs of examples for fitting the nonconformity scorer.
  
      y : numpy array of shape [n_samples]
          Outputs of examples for fitting the nonconformity scorer.
  
      Returns
      -------
      None
      """
      # TODO: incremental?
      self.nc_function.fit(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 2 for calling the BaseIcp obj's underlying member method code with example usage: obj.fit(x, y) without return types