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
docstr: Transform data into random interval features.
  
  Parameters
  ----------
  X : pandas DataFrame or 3d numpy array, input time series
  y : array_like, target values (optional, ignored)
  
  Returns
  -------
  Pandas dataframe of random interval features.
code: def _transform(self, X, y=None):
      """Transform data into random interval features.
  
      Parameters
      ----------
      X : pandas DataFrame or 3d numpy array, input time series
      y : array_like, target values (optional, ignored)
  
      Returns
      -------
      Pandas dataframe of random interval features.
      """
      X_t = []
      for i in range(0, self.n_intervals):
          for j in range(len(self._transformers)):
              t = self._transformers[j].fit_transform(
                  np.expand_dims(
                      X[
                          :,
                          self._dims[i],
                          self._intervals[i][0] : self._intervals[i][1],
                      ],
                      axis=1,
                  ),
                  y,
              )
  
              if isinstance(t, pd.DataFrame):
                  t = t.to_numpy()
  
              if i == 0 and j == 0:
                  X_t = t
              else:
                  X_t = np.concatenate((X_t, t), axis=1)
  
      return pd.DataFrame(X_t)
  
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 3 for calling the RandomIntervals obj's underlying member method code with example usage: obj._transform(X, y) and returns: pd