class_docstr: Moving Block Bootstrapping method for synthetic time series generation.
  
  The Moving Block Bootstrapping (MBB) method introduced in [1]_ is  can be used to
  create synthetic time series that mimic the autocorelation patterns of an observed
  stationary series. This method is frequently combined with other transformations
  e.g. BoxCox and STL to produce synthetic time series similar to the observed time
  series [2]_, [3]_.
  
  The returned panel will be a multiindex dataframe (``pd.DataFrame``) with the
  series_id and time_index as the index and a single column of the time series value.
  The values for series_id are "actual" for the original series and "synthetic_n"
  (where n is an integer) for the generated series.
  See the **Examples** section for example output.
  
  Parameters
  ----------
  n_series : int, optional
      The number of bootstrapped time series that will be generated, by default 10
  block_length : int, optional
      The length of the block in the MBB method, by default None.
      If not provided, the following heuristic is used, the block length will the
      minimum between 2*sp and len(X) - sp.
  sampling_replacement : bool, optional
      Whether the MBB sample is with or without replacement, by default False.
  return_actual : bool, optional
      If True the output will contain the actual time series, by default True.
      The actual time series will be labelled as "actual"
  random_state : int, np.random.RandomState or None, by default None
      Controls the randomness of the estimator
  
  See Also
  --------
  sktime.transformations.bootstrap.STLBootstrapTransformer :
      Transformer that utilises BoxCox, STL and Moving Block Bootstrapping to create
      a panel of similar time series.
  
  References
  ----------
  .. [1] Kunsch HR (1989) The jackknife and the bootstrap for general stationary
      observations. Annals of Statistics 17(3), 1217-1241
  .. [2] Bergmeir, C., Hyndman, R. J., & Benítez, J. M. (2016). Bagging exponential
      smoothing methods using STL decomposition and Box-Cox transformation.
      International Journal of Forecasting, 32(2), 303-312
  .. [3] Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and
      practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3,
      Chapter 12.5. Accessed on February 13th 2022. Accessed on February 13th 2022.
  
  Examples
  --------
  >>> from sktime.transformations.bootstrap import MovingBlockBootstrapTransformer
  >>> from sktime.datasets import load_airline
  >>> from sktime.utils.plotting import plot_series  # doctest: +SKIP
  >>> y = load_airline()
  >>> transformer = MovingBlockBootstrapTransformer(10)
  >>> y_hat = transformer.fit_transform(y)
  >>> series_list = []
  >>> names = []
  >>> for group, series in y_hat.groupby(level=[0], as_index=False):
  ...     series.index = series.index.droplevel(0)
  ...     series_list.append(series)
  ...     names.append(group)
  >>> plot_series(*series_list, labels=names)  # doctest: +SKIP
  (...)
  >>> print(y_hat.head()) # doctest: +NORMALIZE_WHITESPACE
                        Number of airline passengers
  series_id time_index
  actual    1949-01                            112.0
            1949-02                            118.0
            1949-03                            132.0
            1949-04                            129.0
            1949-05                            121.0
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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.
  
  
  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",".","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.
  
  
      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 = [
          {"block_length": 5},
          {"block_length": 1},
          {"block_length": 5, "return_actual": False},
          {"block_length": 5, "sampling_replacement": True},
      ]
  
      return params
  
imports: from copy import copy
  from typing import Tuple, Union
  import numpy
  import pandas
  from sklearn.utils import check_random_state
  from sktime.transformations.base import BaseTransformer
  from sktime.transformations.series.boxcox import BoxCoxTransformer
label_desc: Use image node_id 3 for calling the MovingBlockBootstrapTransformer obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: params