class_docstr: Impute missing values using interpolation.
  
  Parameters
  ----------
  missing_values : None, np.nan, integer or float (default = np.nan)
      The placeholder for the missing values. All occurrences of
      `missing_values` will be imputed. If an integer or a float,
      the input data must not contain NaN or infinity values.
  
  strategy : str or int (default = 'linear')
      Specifies the kind of interpolation as a string
      ('linear', 'nearest', 'zero', 'slinear', 'quadratic', 'cubic',
      'previous', 'next', where 'zero', 'slinear', 'quadratic' and 'cubic'
      refer to a spline interpolation of zeroth, first, second or third
      order; 'previous' and 'next' simply return the previous or next value
      of the point) or as an integer specifying the order of the spline
      interpolator to use. Default is 'linear'.
  
  Examples
  --------
  >>> import numpy as np
  >>> from pyts.preprocessing import InterpolationImputer
  >>> X = [[1, None, 3, 4], [8, None, 4, None]]
  >>> imputer = InterpolationImputer()
  >>> imputer.transform(X)
  array([[1., 2., 3., 4.],
         [8., 6., 4., 2.]])
code: def _check_params(self):
      if self.missing_values is None:
          missing_values = np.nan
          force_all_finite = "allow-nan"
      elif isinstance(
          self.missing_values, (int, np.integer, float, np.floating)
      ):
          if np.isinf(self.missing_values):
              raise ValueError("'missing_values' cannot be infinity.")
          elif np.isnan(self.missing_values):
              force_all_finite = "allow-nan"
              missing_values = np.nan
          else:
              force_all_finite = True
              missing_values = self.missing_values
      else:
          raise ValueError(
              "'missing_values' must be an integer, a float, None or "
              "np.nan (got {0!s})".format(self.missing_values)
          )
      strategy_str_values = [
          "linear",
          "nearest",
          "zero",
          "slinear",
          "quadratic",
          "cubic",
          "previous",
          "next",
      ]
      if not (
          (isinstance(self.strategy, (int, np.integer)))
          or (self.strategy in strategy_str_values)
      ):
          raise ValueError(
              "'strategy' must be an integer or one of 'linear', 'nearest', "
              "'zero', 'slinear', 'quadratic', 'cubic', 'previous', 'next' "
              "(got {0})".format(self.strategy)
          )
      return missing_values, force_all_finite
  
file_path: pyts/pyts/preprocessing/imputer.py
imports: import numpy
  from scipy.interpolate import interp1d
  from sklearn.base import BaseEstimator
  from sklearn.impute import MissingIndicator
  from sklearn.utils.validation import check_array
  from ..base import UnivariateTransformerMixin
label_desc: Use image node_id 4 for calling the InterpolationImputer obj's underlying member method code with example usage: obj._check_params() and returns: missing_values,  force_all_finite