class_docstr: Time series interpolator/re-sampler.
  
  Transformer that rescales series for another number of points.
  For each cell in dataframe transformer fits scipy linear interp1d
  and samples user defined number of points. Points are generated
  by numpy.linspace.
  
  After transformation each cell will be a pd.Series of given length.
  Indices of the pd.Series will be changed to integer indices.
  
  Parameters
  ----------
  length : integer, the length of time series to resize to.
docstr: Take series in each cell, train linear interpolation and samples n.
  
  Parameters
  ----------
  X : nested pandas DataFrame of shape [n_samples, n_features]
      Nested dataframe with time series in cells, following nested_univ format.
  y : ignored argument for interface compatibility
  
  Returns
  -------
  pandas DataFrame : Transformed pandas DataFrame of shape [n_samples, n_features]
      follows nested_univ format
code: def _transform(self, X, y=None):
      """Take series in each cell, train linear interpolation and samples n.
  
      Parameters
      ----------
      X : nested pandas DataFrame of shape [n_samples, n_features]
          Nested dataframe with time series in cells, following nested_univ format.
      y : ignored argument for interface compatibility
  
      Returns
      -------
      pandas DataFrame : Transformed pandas DataFrame of shape [n_samples, n_features]
          follows nested_univ format
      """
      return df_map(X)(self._resize_cell)
  
file_path: sktime/sktime/transformations/panel/interpolate.py
imports: import numpy
  import pandas
  from sktime.transformations.base import BaseTransformer
  from sktime.utils.pandas import df_map
label_desc: Use image node_id 3 for calling the TSInterpolator obj's underlying member method code with example usage: obj._transform(X, y) and returns: df_map