class_docstr: Bins time series and aggregates by bin.
  
  In `transform`, applies `groupby` with `aggfunc` on the temporal coordinate.
  
  More precisely:
  `bins` encodes bins :math:`B_1, \dots, B_k` where :math:`B_i` are intervals,
  in the reals or in a temporal (time stamp) range.
  
  In `transform`, the estimator `TimeBinAggregate` collects values
  at time stamps of `X` falling into :math:`B_i` as a sample :math:`S_i`,
  and then applies `aggfunc` to :math:`S_i` to obtain an aggregate value :math:`v_i`.
  The transformed series are values :math:`v_i` at time stamps :math:`t_i`,
  determined from :math:`B_i` per the rule in `return_index`.
  
  Parameters
  ----------
  bins : 1D array-like or pd.IntervalIndex
      if 1D array-like, is interpreted as breaks of bins
      breaks of bins defining intervals considered by aggfunc
  aggfunc : callable *1D array-like -> float), optional, default=np.mean
      Function used to aggregate the values in intervals.
      Should have signature 1D -> float and defaults
      to mean if None
  return_index : str, one of the below; optional, default="range"
      "range" = RangeIndex with bins indexed in same order as in `bins`
      "bin_start" = transformed pd.DataFrame will be indexed by bin starts
      "bin_end" = transformed pd.DataFrame will be indexed by bin starts
      "bin_mid" = transformed pd.DataFrame will be indexed by bin midpoints
      "bin" = transformed pd.DataFrame will have `bins` as `IntervalIndex`
  
  Example
  -------
  from sktime.datatypes import get_examples
  from sktime.transformations.series.binning import TimeBinAggregate
  
  bins = [0, 2, 4]
  X = get_examples("pd.DataFrame")[0]
  
  t = TimeBinAggregate([-1, 2, 10])
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`
docstr_tok: ["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","`"]
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`
      """
      params1 = {"bins": [0, 1]}
  
      params2 = {
          "bins": [0, 2, 4],
          "aggfunc": np.sum,
          "return_index": "bin_start",
      }
      return [params1, params2]
  
file_path: sktime/sktime/transformations/series/binning.py
imports: import warnings
  import numpy
  import pandas
  from sktime.transformations.base import BaseTransformer
label_desc: Use image node_id 3 for calling the TimeBinAggregate obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list