class_docstr: Sliding window segmenter transformer.
  
  This class is to transform a univariate series into a
  multivariate one by extracting sets of subsequences.
  It does this by firstly padding the time series on either end
  floor(window_length/2) times. Then it performs a sliding
  window of size window_length and hop size 1.
  
  e.g. if window_length = 3
  
  S = 1,2,3,4,5, floor(3/2) = 1 so S would be padded as
  
  1,1,2,3,4,5,5
  
  then SlidingWindowSegmenter would extract the following:
  
  (1,1,2),(1,2,3),(2,3,4),(3,4,5),(4,5,5)
  
  the time series is now a multivariate one.
  
  Parameters
  ----------
  window_length : int, optional, default=5.
      length of sliding window interval
  
  Used by the ShapeDTW algorithm.
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code: def __init__(self, window_length=5):
      self.window_length = window_length
      super().__init__()
  
imports: import math
  import numpy
  import pandas
  from sklearn.utils import check_random_state
  from sktime.datatypes._utilities import get_time_index
  from sktime.transformations._delegate import _DelegatedTransformer
  from sktime.transformations.base import BaseTransformer
  from sktime.utils.validation import check_window_length
label_desc: Use image node_id 1 to create a new SlidingWindowSegmenter object from inherited base classes: BaseTransformer with example: obj = SlidingWindowSegmenter(window_length)