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.]])
docstr: Pass.
  
  Parameters
  ----------
  X
      Ignored
  
  y
      Ignored
  
  Returns
  -------
  self : object
docstr_tok: ["Pass",".","Parameters","--","--","--","--","--","X","Ignored","y","Ignored","Returns","--","--","--","-","self",":","object"]
code: def fit(self, X=None, y=None):
      """Pass.
  
      Parameters
      ----------
      X
          Ignored
  
      y
          Ignored
  
      Returns
      -------
      self : object
  
      """
      return self
  
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 2 for calling the InterpolationImputer obj's underlying member method code with example usage: obj.fit(X, y) and returns: self