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
code: def __init__(
      self,
      n_intervals=100,
      transformers=None,
      random_state=None,
      n_jobs=1,
  ):
      self.n_intervals = n_intervals
      self.transformers = transformers
  
      self.random_state = random_state
      self.n_jobs = n_jobs
  
      self._transformers = transformers
      self._intervals = []
      self._dims = []
  
      super().__init__()
  
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 1 to create a new RandomIntervals object from inherited base classes: BaseTransformer with example: obj = RandomIntervals(n_intervals, transformers, random_state, n_jobs)