class_docstr: Random interval feature transformer.
  
  Extracts intervals with random length, position and dimension from series in fit.
  Transforms each interval subseries using the given transformer(s) and concatenates
  them into a feature vector in transform.
  
  Currently, the transform is re-fit for every interval in transform. As such, it may
  not be suitable for some supervised transformers in its current state.
  
  Parameters
  ----------
  n_intervals : int, default=100,
      The number of intervals of random length, position and dimension to be
      extracted.
  transformers : transformer or list of transformers, default=None,
      Transformer(s) used to extract features from each interval. If None, defaults to
      the SummaryTransformer using
      [mean, median, min, max, std, 25% quantile, 75% quantile]
  n_jobs : int, default=1
      The number of jobs to run in parallel for both `fit` and `predict`.
      ``-1`` means using all processors.
  random_state : int or None, default=None
      Seed for random, integer.
  
  See Also
  --------
  SupervisedIntervals
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`
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`
      """
      return {"n_intervals": 3}
  
file_path: sktime/sktime/transformations/panel/random_intervals.py
imports: import numpy
  import pandas
  from sklearn.utils import check_random_state
  from sktime.base._base import _clone_estimator
  from sktime.transformations.base import BaseTransformer
  from sktime.transformations.series.summarize import SummaryTransformer
label_desc: Use image node_id 4 for calling the RandomIntervals obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: dict