class_docstr: Random interval feature extractor transform.
  
  Transformer that segments time-series into random intervals
  and subsequently extracts series-to-primitives features from each interval.
  
  n_intervals: str{'sqrt', 'log', 'random'}, int or float, optional (
  default='sqrt')
      Number of random intervals to generate, where m is length of time
      series:
      - If "log", log of m is used.
      - If "sqrt", sqrt of m is used.
      - If "random", random number of intervals is generated.
      - If int, n_intervals intervals are generated.
      - If float, int(n_intervals * m) is used with n_intervals giving the
      fraction of intervals of the
      time series length.
  
      For all arguments relative to the length of the time series,
      the generated number of intervals is
      always at least 1.
  
  features: list of functions, optional (default=None)
      Applies each function to random intervals to extract features.
      If None, the mean is extracted.
  
  random_state: : int, RandomState instance, optional (default=None)
      - If int, random_state is the seed used by the random number generator;
      - If RandomState instance, random_state is the random number generator;
      - If None, the random number generator is the RandomState instance used
      by `np.random`.
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code: def __init__(
      self,
      n_intervals="sqrt",
      min_length=None,
      max_length=None,
      features=None,
      random_state=None,
  ):
      self.n_intervals = n_intervals
      self.min_length = min_length
      self.max_length = max_length
      self.random_state = random_state
      self.features = features
      super().__init__()
  
imports: import numpy
  import pandas
  from joblib import Parallel, delayed
  from sktime.datatypes import convert_to
  from sktime.transformations.base import BaseTransformer
  from sktime.transformations.panel.segment import RandomIntervalSegmenter
  from sktime.utils.pandas import df_map
label_desc: Use image node_id 1 to create a new RandomIntervalFeatureExtractor object from inherited base classes: BaseTransformer with example: obj = RandomIntervalFeatureExtractor(n_intervals, min_length, max_length, features, random_state)