class_docstr: Feature extractor over a window.
  
  This transformer extracts 3 features from each window: the mean, the
  standard deviation and the slope.
  
  Parameters
  ----------
  n_windows : int or float (default = 1.)
      The number of windows from which features are extracted.
  
  min_window_size : int or float (default = 1)
      The minimum length of the windows. If float, it represents a percentage
      of the size of each time series.
  
  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
  ----------
  indices_ : array, shape = (n_windows, 2)
      The indices for the windows.
      The first column consists of the starting indices (included)
      of the windows. The second column consists of the ending indices
      (excluded) of the windows.
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code: def __init__(
      self, n_windows=1.0, min_window_size=1, random_state=None
  ):
      self.n_windows = n_windows
      self.min_window_size = min_window_size
      self.random_state = random_state
  
imports: from math import ceil
  from numba import njit
  import numpy
  from sklearn.base import BaseEstimator
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.pipeline import Pipeline
  from sklearn.utils.validation import check_array, check_is_fitted, check_random_state
  from ..base import UnivariateClassifierMixin, UnivariateTransformerMixin
label_desc: Use image node_id 1 to create a new WindowFeatureExtractor object from inherited base classes: BaseEstimator, UnivariateTransformerMixin with example: obj = WindowFeatureExtractor(n_windows, min_window_size, random_state)