class_docstr: Feature extractor over the intervals of a subsequence.
  
  This transformer extracts 3 features from each interval of each
  subsequence: the mean, the standard deviation and the slope.
  
  Parameters
  ----------
  min_subsequence_size : int or float (default = 0.5)
      The minimum length of the subsequences. If float, it represents a
      percentage of the size of each time series. Note that the actual
      minimum length of the subsequences may be slighty lower due to the use
      of the floor function.
  
  min_interval_size : int or float (default = 0.1)
      The minimum length of the intervals. If float, it represents a
      percentage of the size of each time series. Must be lower than
      `min_subsequence_size`.
  
  n_subsequences : 'auto', int or float (default = 'auto')
      The number of considered subsequences. If 'auto', it is automatically
      calculated given the size of the time series and the values of the
      parameters. If float, it represents a percentage of the size of each
      time series.
  
  bins : int or array-like (default = 10)
      If bins is an int, it defines the number of equal-width bins in range
      [0, 1]. If bins is array-like, it defines a monotonically increasing
      array of bin edges, including the rightmost edge, allowing for
      non-uniform bin widths.
  
  random_state : None, int or RandomState instance (default = None)
      The seed of the pseudo random number generator to use when shuffling
      the data. 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`.
  
  Attributes
  ----------
  interval_indices_ : array, shape = (n_subsequences, n_intervals + 1)
      The indices for the intervals of each subsequence.
  
  min_subsequence_size_ : int
      The actual minimum length of the subsequences.
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docstr: Transform the provided data.
  
  It extracts the three features from all the selected windows
  for all the samples.
  
  Parameters
  ----------
  X : array-like, shape = (n_samples, n_timestamps)
      Univariate time series.
  
  Returns
  -------
  X_new : array, shape = (n_samples * n_subseries, 3 * n_intervals + 4)
      Extracted features.
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code: def transform(self, X):
      """Transform the provided data.
  
      It extracts the three features from all the selected windows
      for all the samples.
  
      Parameters
      ----------
      X : array-like, shape = (n_samples, n_timestamps)
          Univariate time series.
  
      Returns
      -------
      X_new : array, shape = (n_samples * n_subseries, 3 * n_intervals + 4)
          Extracted features.
  
      """
      X = check_array(X, dtype="float64")
      n_samples = X.shape[0]
  
      # Extract the features from each interval
      interval_indices = np.array(
          [
              self.interval_indices_[:, :-1],
              self.interval_indices_[:, 1:],
          ]
      )
  
      n_subseries, n_intervals = interval_indices.shape[1:]
      X_features = extract_features(
          X, interval_indices, n_samples, n_subseries, n_intervals
      )
      return X_features
  
imports: from math import ceil
  from numba import njit
  import numpy
  from sklearn.base import BaseEstimator
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.utils.validation import check_array, check_is_fitted, check_random_state, check_X_y
  from ..base import UnivariateClassifierMixin, UnivariateTransformerMixin
label_desc: Use image node_id 3 for calling the IntervalFeatureExtractor obj's underlying member method code with example usage: obj.transform(X) and returns: X_features