class_docstr: Interval segmentation transformer.
  
  Parameters
  ----------
  intervals : int, np.ndarray or list of np.ndarrays with one for each
  column of input data.
      Intervals to generate.
      - If int, intervals gives the number of generated intervals.
      - If ndarray, 2d np.ndarray [n_intervals, 2] with rows giving
      intervals, the first column giving start points,
      and the second column giving end points of intervals
  
  Examples
  --------
  >>> from sktime.utils._testing.panel import _make_panel
  >>> from sktime.transformations.panel.segment import IntervalSegmenter
  
  >>> X = _make_panel()
  >>> t = IntervalSegmenter()
  >>> Xt = t.fit_transform(X)
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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`
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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.
  
  
      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`
      """
      # small number of intervals for testing
      params = {"intervals": 2}
      params2 = {"intervals": 3}
      return [params, params2]
  
imports: import math
  import numpy
  import pandas
  from sklearn.utils import check_random_state
  from sktime.datatypes._utilities import get_time_index
  from sktime.transformations._delegate import _DelegatedTransformer
  from sktime.transformations.base import BaseTransformer
  from sktime.utils.validation import check_window_length
label_desc: Use image node_id 4 for calling the IntervalSegmenter obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list