class_docstr: MrSQM = Multiple Representations Sequence Miner.
  
  Direct Interface to MrSQMClassifier from mrsqm.
  Note: mrsqm itself is copyleft (GPL3). This interface is permissive license (BSD3).
  
  MrSQM is an efficient time series classifier utilizing symbolic representations of
  time series. MrSQM implements four different feature selection strategies =
  (R,S,RS,SR) that can quickly select subsequences from multiple symbolic
  representations of time series data.
  
  Parameters
  ----------
  strat               : str, one of 'R','S','SR', or 'RS', default="RS"
      feature selection strategy. By default set to 'RS'.
      R and S are single-stage filters while RS and SR are two-stage filters.
  features_per_rep    : int, default=500
      (maximum) number of features selected per representation.
  selection_per_rep   : int, default=2000
      (maximum) number of candidate features selected per representation.
      Only applied in two stages strategies (RS and SR), otherwise ignored.
  nsax                : int, default=1
      number of representations produced by sax transformation.
  nsfa                : int, default=0
      number of representations produced by sfa transformation.
  custom_config       : dict, default=None
      customized parameters for the symbolic transformation.
  random_state        : int, default=None.
      random seed for the classifier.
  sfa_norm            : bool, default=True.
      whether to apply time series normalisation (standardisation).
  
  References
  ----------
  .. [1] Thach Le Nguyen and Georgiana Ifrim.
     "MrSQM: Fast Time Series Classification with Symbolic Representations
     and Efficient Sequence Mining" arXiv preprint arXiv:2109.01036 (2021).
  .. [2] Thach Le Nguyen and Georgiana Ifrim.
     "Fast Time Series Classification with Random Symbolic Subsequences".
     AALTD 2022.
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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.
      For classifiers, a "default" set of parameters should be provided for
      general testing, and a "results_comparison" set for comparing against
      previously recorded results if the general set does not produce suitable
      probabilities to compare against.
  
  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.
          For classifiers, a "default" set of parameters should be provided for
          general testing, and a "results_comparison" set for comparing against
          previously recorded results if the general set does not produce suitable
          probabilities to compare against.
  
      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 = {}
  
      params2 = {
          "strat": "SR",
          "features_per_rep": 200,
          "selection_per_rep": 1000,
          "nsax": 2,
          "nsfa": 1,
          "sfa_norm": False,
      }
  
      return [params1, params2]
  
imports: from sktime.classification._delegate import _DelegatedClassifier
label_desc: Use image node_id 3 for calling the MrSQM obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list