class_docstr: Fresh Pipeline with RotatIoN forest Classifier.
  
  This classifier simply transforms the input data using the TSFresh [1]_
  transformer with comprehensive features and builds a RotationForest estimator using
  the transformed data.
  
  Parameters
  ----------
  default_fc_parameters : str, default="comprehensive"
      Set of TSFresh features to be extracted, options are "minimal", "efficient" or
      "comprehensive".
  n_estimators : int, default=200
      Number of estimators for the RotationForest ensemble.
  verbose : int, default=0
      Level of output printed to the console (for information only)
  n_jobs : int, default=1
      The number of jobs to run in parallel for both `fit` and `predict`.
      ``-1`` means using all processors.
  chunksize : int or None, default=None
      Number of series processed in each parallel TSFresh job, should be optimised
      for efficient parallelisation.
  random_state : int or None, default=None
      Seed for random, integer.
  
  Attributes
  ----------
  n_classes_ : int
      Number of classes. Extracted from the data.
  classes_ : ndarray of shape (n_classes_)
      Holds the label for each class.
  
  See Also
  --------
  TSFreshFeatureExtractor, TSFreshClassifier, RotationForest
  
  References
  ----------
  .. [1] Christ, Maximilian, et al. "Time series feature extraction on basis of
      scalable hypothesis tests (tsfresh–a python package)." Neurocomputing 307
      (2018): 72-77.
      https://www.sciencedirect.com/science/article/pii/S0925231218304843
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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`.
      """
      if parameter_set == "results_comparison":
          return {
              "n_estimators": 10,
              "default_fc_parameters": "minimal",
          }
      else:
          return {
              "n_estimators": 2,
              "default_fc_parameters": "minimal",
              "save_transformed_data": True,
          }
  
file_path: sktime/sktime/classification/feature_based/_fresh_prince.py
imports: import numpy
  from sktime.classification.base import BaseClassifier
  from sktime.classification.sklearn import RotationForest
  from sktime.transformations.panel.tsfresh import TSFreshFeatureExtractor
  from sktime.utils.validation.panel import check_X_y
label_desc: Use image node_id 6 for calling the FreshPRINCE obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: dict, dict