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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code: def _check_params(self, X, n_timestamps):
      if not isinstance(
          self.min_subsequence_size,
          (int, np.integer, float, np.floating),
      ):
          raise TypeError(
              "'min_subsequence_size' must be an integer or a " "float."
          )
      if isinstance(self.min_subsequence_size, (int, np.integer)):
          if not 1 <= self.min_subsequence_size <= n_timestamps:
              raise ValueError(
                  "If 'min_subsequence_size' is an integer, it must be "
                  "greater than or equal to 1 and lower than or equal to "
                  "n_timestamps (got {0}).".format(
                      self.min_subsequence_size
                  )
              )
          min_subsequence_size = self.min_subsequence_size
      else:
          if not (0 < self.min_subsequence_size <= 1.0):
              raise ValueError(
                  "If 'min_subsequence_size' is a float, it must be greater "
                  "than 0 and lower than or equal to 1 (got {0}).".format(
                      self.min_subsequence_size
                  )
              )
          min_subsequence_size = ceil(
              self.min_subsequence_size * n_timestamps
          )
  
      if not isinstance(
          self.min_interval_size, (int, np.integer, float, np.floating)
      ):
          raise TypeError(
              "'min_interval_size' must be an integer or a " "float."
          )
      if isinstance(self.min_interval_size, (int, np.integer)):
          if self.min_interval_size < 1:
              raise ValueError(
                  "If 'min_interval_size' is an integer, it must be "
                  "positive (got {0}).".format(self.min_interval_size)
              )
          min_interval_size = self.min_interval_size
      else:
          if self.min_interval_size <= 0:
              raise ValueError(
                  "If 'min_interval_size' is a float, it must be greater "
                  "than 0 (got {0}).".format(self.min_interval_size)
              )
          min_interval_size = ceil(
              self.min_interval_size * n_timestamps
          )
  
      if min_interval_size > min_subsequence_size:
          raise ValueError(
              "'min_interval_size' must be lower than or equal "
              "to 'min_subsequence_size' ({} > {}).".format(
                  min_interval_size, min_subsequence_size
              )
          )
  
      n_subsequences_auto = (
          isinstance(self.n_subsequences, str)
          and self.n_subsequences == "auto"
      )
      if not (
          n_subsequences_auto
          or isinstance(
              self.n_subsequences, (int, np.integer, float, np.floating)
          )
      ):
          raise TypeError(
              "'n_subsequences' must be 'auto', an integer or a "
              "float."
          )
      if n_subsequences_auto:
          n_subsequences = "auto"
      elif isinstance(self.n_subsequences, (int, np.integer)):
          if self.n_subsequences < 1:
              raise ValueError(
                  "If 'n_subsequences' is an integer, it must be positive "
                  "(got {0}).".format(self.n_subsequences)
              )
          n_subsequences = self.n_subsequences
      else:
          if self.n_subsequences <= 0:
              raise ValueError(
                  "If 'n_subsequences' is a float, it must be greater "
                  "than 0 (got {0}).".format(self.n_subsequences)
              )
          n_subsequences = ceil(self.n_subsequences * n_timestamps)
  
      rng = check_random_state(self.random_state)
  
      return (
          min_subsequence_size,
          min_interval_size,
          n_subsequences,
          rng,
      )
  
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 4 for calling the IntervalFeatureExtractor obj's underlying member method code with example usage: obj._check_params(X, n_timestamps) and returns: min_subsequence_size,  min_interval_size,  n_subsequences,  rng