class_docstr: Turns time series/panel data into tabular data based on intervals.
  
  This estimator converts nested pandas dataframe containing
  time-series/panel data with numpy arrays or pandas Series in
  dataframe cells into a tabular pandas dataframe with only primitives in
  cells. The primitives are calculated based on Intervals defined
  by the IntervalIndex and aggregated by aggfunc.
  
  This is useful for transforming time-series/panel data
  into a format that is accepted by standard validation learning
  algorithms (as in sklearn).
  
  Parameters
  ----------
  idx : pd.IntervalIndex
      IntervalIndex defining intervals considered by aggfunc
  aggfunc : callable
      Function used to aggregate the values in intervals.
      Should have signature 1D -> float and defaults
      to mean if None
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docstr: Return testing parameter settings for the estimator.
  
  Parameters
  ----------
  parameter_set : str, default="default"
      Name of the set of test parameters to return, for use in tests. If no
      special parameters are defined for a value, will return `"default"` set.
      There are currently no reserved values for transformers.
  
  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`
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code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the estimator.
  
      Parameters
      ----------
      parameter_set : str, default="default"
          Name of the set of test parameters to return, for use in tests. If no
          special parameters are defined for a value, will return `"default"` set.
          There are currently no reserved values for transformers.
  
      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`
      """
      import pandas as pd
  
      idx = pd.interval_range(start=0, end=100, freq=10, closed="left")
      params = {"idx": idx}
      return params
  
file_path: sktime/sktime/transformations/panel/reduce.py
imports: import warnings
  import numpy
  import pandas
  from sktime.datatypes import convert, convert_to
  from sktime.transformations.base import BaseTransformer
  from sktime.utils.pandas import df_map
label_desc: Use image node_id 3 for calling the TimeBinner obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: params