class_docstr: Finds the "elbow" or "knee" which is a value corresponding to the point of maximum curvature
  in an elbow curve, using knee point detection algorithm. This point is accessible via the
  `knee` attribute.
  
  Parameters
  ----------
  x : list
     A list of k values representing the no. of clusters in KMeans Clustering algorithm.
  
  y : list
     A list of k scores corresponding to each value of k. The type of k scores are determined 
     by the metric parameter from the KElbowVisualizer class.
  
  S : float, default: 1.0
     Sensitivity parameter that allows us to adjust how aggressive we want KneeLocator to
     be when detecting "knees" or "elbows".
  
  curve_nature : string, default: 'concave'
     A string that determines the nature of the elbow curve in which "knee" or "elbow" is
     to be found.
  
  curve_direction : string, default: 'increasing'
     A string that determines the increasing or decreasing nature of the elbow curve in
     which "knee" or "elbow" is to be found.
  
  online : bool, default: False
      kneed will correct old knee points if True, will return first knee if False
  
  Notes
  -----
  The KneeLocator is implemented using the "knee point detection algorithm" which can be read at
  `<https://www1.icsi.berkeley.edu/~barath/papers/kneedle-simplex11.pdf>`
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code: def all_norm_elbows_y(self):
      return self.all_norm_knees_y
  
imports: import numpy
  from scipy import interpolate
  from scipy.signal import argrelextrema
  import warnings
  from yellowbrick.exceptions import YellowbrickWarning
label_desc: Use image node_id 14 for calling the KneeLocator obj's underlying member method code with example usage: obj.all_norm_elbows_y() and returns: self