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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code: def __init__(self, intervals=10):
      self.intervals = intervals
      self._time_index = []
      super().__init__()
  
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 1 to create a new IntervalSegmenter object from inherited base classes: BaseTransformer with example: obj = IntervalSegmenter(intervals)