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: Transform input series.
  
  Transform X, segments time-series in each column into random
  intervals using interval indices generated
  during `fit`.
  
  Parameters
  ----------
  X : nested pandas DataFrame of shape [n_instances, n_features]
      each cell of X must contain pandas.Series
      Data to be transformed
  y : ignored argument for interface compatibility
  
  Returns
  -------
  Xt : pandas DataFrame
    Transformed pandas DataFrame with same number of rows and one
    column for each generated interval.
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code: def _transform(self, X, y=None):
      """Transform input series.
  
      Transform X, segments time-series in each column into random
      intervals using interval indices generated
      during `fit`.
  
      Parameters
      ----------
      X : nested pandas DataFrame of shape [n_instances, n_features]
          each cell of X must contain pandas.Series
          Data to be transformed
      y : ignored argument for interface compatibility
  
      Returns
      -------
      Xt : pandas DataFrame
        Transformed pandas DataFrame with same number of rows and one
        column for each generated interval.
      """
      segments = []
  
      for start, end in self.intervals_:
          nlevels = X.index.nlevels
          seg = X.groupby(level=list(range(nlevels - 1)))
  
          def subset(x):
              return x.iloc[start:end].reset_index(
                  drop=True
              )  # noqa B023
  
          seg = seg.apply(subset)
          seg.columns = [f"{X.columns[0]}_{start}_{end}"]
  
          segments = segments + [seg]
  
      Xt = pd.concat(segments, axis=1)
      return Xt
  
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 3 for calling the IntervalSegmenter obj's underlying member method code with example usage: obj._transform(X, y) and returns: Xt, x