class_docstr: Inductive conformal forecasting.
  
  Parameters
  ----------
  nc_function : BaseScorer
      Nonconformity scorer object used to calculate nonconformity of
      calibration examples and test patterns. Should implement ``fit(x, y)``,
      ``calc_nc(x, y)`` and ``predict(x, nc_scores, significance)``.
  
  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, horizon_length]
      Outputs of calibration set.
  
  nc_function : BaseScorer
      Nonconformity scorer object used to calculate nonconformity scores.
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docstr: After calibration, handles incomplete target information by imputing the row-wise mean.
docstr_tok: ["After","calibration",",","handles","incomplete","target","information","by","imputing","the","row-wise","mean","."]
code: def calibrate(self, x, y, increment=False):
      """
      After calibration, handles incomplete target information by imputing the row-wise mean.
      """  # noqa
      super(IcpTSRegressor, self).calibrate(x, y, increment)
      for k, v in self.cal_scores.items():
          row_mean = np.nanmean(v, axis=1)
          idxs = np.where(np.isnan(v))
          v[idxs] = np.take(row_mean, idxs[0])
          self.cal_scores[k] = v
  
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 IcpTSRegressor obj's underlying member method code with example usage: obj.calibrate(x, y, increment) without return types