code: def _produce_intervals(
      n_estimators,
      min_interval,
      max_interval,
      series_length,
      rng,
      method=3,
  ):
      """Private function used to produce intervals for all trees."""
      intervals = np.empty((n_estimators, 2), dtype=int)
      if method == 0:
          # just keep it as a backup, untested
          intervals[:, 0] = rng.randint(
              series_length - min_interval, size=n_estimators
          )
          intervals[:, 1] = rng.randint(
              intervals[:, 0] + min_interval,
              series_length,
              size=n_estimators,
          )
      elif method == 3:
          bools = rng.randint(2, size=n_estimators)
          true = np.where(bools == 1)[0]
          intervals[true, 0] = rng.randint(
              series_length - min_interval, size=true.size
          )
          interval_range = np.fmin(
              series_length - intervals[true, 0], max_interval
          )
          length = rng.randint(min_interval, interval_range)
          intervals[true, 1] = intervals[true, 0] + length
  
          false = np.where(bools == 0)[0]
          intervals[false, 1] = rng.randint(
              min_interval, series_length, size=false.size
          )
          interval_range = np.fmin(intervals[false, 1], max_interval)
          min_mask = interval_range == min_interval
          length = np.empty(false.size)
          length[min_mask] = 3
          length[~min_mask] = rng.randint(
              min_interval, interval_range[~min_mask]
          )
          intervals[false, 0] = intervals[false, 1] - length
  
      return intervals
  
imports: import numpy
  from joblib import Parallel, delayed
  from sklearn.base import clone
  from sklearn.ensemble._base import _partition_estimators
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils.validation import check_random_state
  from sktime.classification.base import BaseClassifier
label_desc: Use image node_id 6 for calling a global function with example usage: _produce_intervals(n_estimators, min_interval, max_interval, series_length, rng, method) and returns: intervals