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: Resize a single array.
  
  Resizes the array. Firstly 1d linear interpolation is fitted on
     original array as y and numpy.linspace(0, 1, len(cell)) as x.
     Then user defined number of points is sampled in
     numpy.linspace(0, 1, length) and returned into cell as numpy array.
  
  Parameters
  ----------
  cell : array-like
  
  Returns
  -------
  numpy.array : with user defined size
code: def _resize_cell(self, cell):
      """Resize a single array.
  
      Resizes the array. Firstly 1d linear interpolation is fitted on
         original array as y and numpy.linspace(0, 1, len(cell)) as x.
         Then user defined number of points is sampled in
         numpy.linspace(0, 1, length) and returned into cell as numpy array.
  
      Parameters
      ----------
      cell : array-like
  
      Returns
      -------
      numpy.array : with user defined size
      """
      from scipy import interpolate
  
      f = interpolate.interp1d(
          list(np.linspace(0, 1, len(cell))), cell.to_numpy()
      )
      Xt = f(np.linspace(0, 1, self.length))
      return pd.Series(Xt)
  
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 2 for calling the TSInterpolator obj's underlying member method code with example usage: obj._resize_cell(cell) and returns: pd