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: Return testing parameter settings for the estimator.
  
  Returns
  -------
  params : dict or list of dict, default={}
      Parameters to create testing instances of the class.
      Each dict are parameters to construct an "interesting" test instance, i.e.,
      `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
      `create_test_instance` uses the first (or only) dictionary in `params`.
code: def get_test_params(cls):
      """Return testing parameter settings for the estimator.
  
      Returns
      -------
      params : dict or list of dict, default={}
          Parameters to create testing instances of the class.
          Each dict are parameters to construct an "interesting" test instance, i.e.,
          `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
          `create_test_instance` uses the first (or only) dictionary in `params`.
      """
      params = {"length": 10}
      return params
  
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 4 for calling the TSInterpolator obj's underlying member method code with example usage: obj.get_test_params(cls) and returns: params