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: Fit the model according to the given training data.
  
  It generates the indices of the windows from which the features will be
  extracted.
  
  Parameters
  ----------
  X : array-like, shape = (n_samples, n_timestamps)
      Univariate time series.
  
  y
      Ignored
  
  Returns
  -------
  self : object
docstr_tok: ["Fit","the","model","according","to","the","given","training","data",".","It","generates","the","indices","of","the","windows","from","which","the","features","will","be","extracted",".","Parameters","--","--","--","--","--","X",":","array-like",",","shape","=","(","n_samples",",","n_timestamps",")","Univariate","time","series",".","y","Ignored","Returns","--","--","--","-","self",":","object"]
code: def fit(self, X, y=None):
      """Fit the model according to the given training data.
  
      It generates the indices of the windows from which the features will be
      extracted.
  
      Parameters
      ----------
      X : array-like, shape = (n_samples, n_timestamps)
          Univariate time series.
  
      y
          Ignored
  
      Returns
      -------
      self : object
  
      """
      X = check_array(X, dtype="float64")
      n_timestamps = X.shape[1]
  
      (
          min_subsequence_size,
          min_interval_size,
          n_subsequences,
          rng,
      ) = self._check_params(X, n_timestamps)
  
      # Define the parameters of the subseries and intervals
      n_intervals = int(min_subsequence_size / min_interval_size)
      min_subseries_size = min_interval_size * n_intervals
      if n_subsequences == "auto":
          n_subseries = max(
              1, n_timestamps // min_interval_size - n_intervals
          )
      else:
          n_subseries = n_subsequences
  
      # Generate the indices of the subseries
      subseries_start = rng.randint(
          0, n_timestamps - min_subseries_size + 1, size=n_subseries
      )
      subseries_end = rng.randint(
          subseries_start + min_subseries_size,
          n_timestamps + 1,
          size=n_subseries,
      )
  
      # Generate the indices of the intervals
      interval_indices = np.linspace(
          subseries_start, subseries_end, n_intervals + 1, axis=1
      ).astype("int64")
  
      # Save the relevant parameters as attributes
      self.interval_indices_ = interval_indices
      self.min_subsequence_size_ = min_subsequence_size
  
      return self
  
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 2 for calling the IntervalFeatureExtractor obj's underlying member method code with example usage: obj.fit(X, y) and returns: self