code: def plot_elbow_curve(
      clf,
      X,
      title="Elbow Plot",
      cluster_ranges=None,
      ax=None,
      figsize=None,
      title_fontsize="large",
      text_fontsize="medium",
  ):
      """Plots elbow curve of different values of K for KMeans clustering.
  
      Args:
          clf: Clusterer instance that implements ``fit`` and ``fit_predict``
              methods and a ``score`` parameter.
  
          X (array-like, shape (n_samples, n_features)):
              Data to cluster, where n_samples is the number of samples and
              n_features is the number of features.
  
          title (string, optional): Title of the generated plot. Defaults to
              "Elbow Plot"
  
          cluster_ranges (None or :obj:`list` of int, optional): List of
              n_clusters for which to plot the explained variances. Defaults to
              ``range(1, 12, 2)``.
  
          copy (boolean, optional): Determines whether ``fit`` is used on
              **clf** or on a copy of **clf**.
  
          ax (:class:`matplotlib.axes.Axes`, optional): The axes upon which to
              plot the curve. If None, the plot is drawn on a new set of axes.
  
          figsize (2-tuple, optional): Tuple denoting figure size of the plot
              e.g. (6, 6). Defaults to ``None``.
  
          title_fontsize (string or int, optional): Matplotlib-style fontsizes.
              Use e.g. "small", "medium", "large" or integer-values. Defaults to
              "large".
  
          text_fontsize (string or int, optional): Matplotlib-style fontsizes.
              Use e.g. "small", "medium", "large" or integer-values. Defaults to
              "medium".
  
      Returns:
          ax (:class:`matplotlib.axes.Axes`): The axes on which the plot was
              drawn.
  
      Example:
          >>> import scikitplot.plotters as skplt
          >>> kmeans = KMeans(random_state=1)
          >>> skplt.plot_elbow_curve(kmeans, cluster_ranges=range(1, 11))
          <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
          >>> plt.show()
  
          .. image:: _static/examples/plot_elbow_curve.png
             :align: center
             :alt: Elbow Curve
      """
      if cluster_ranges is None:
          cluster_ranges = range(1, 12, 2)
      else:
          cluster_ranges = sorted(cluster_ranges)
  
      if not hasattr(clf, "n_clusters"):
          raise TypeError(
              '"n_clusters" attribute not in classifier. '
              "Cannot plot elbow method."
          )
  
      clfs = []
      for i in cluster_ranges:
          current_clf = clone(clf)
          setattr(current_clf, "n_clusters", i)
          clfs.append(current_clf.fit(X).score(X))
  
      if ax is None:
          fig, ax = plt.subplots(1, 1, figsize=figsize)
  
      ax.set_title(title, fontsize=title_fontsize)
      ax.plot(cluster_ranges, np.absolute(clfs), "b*-")
      ax.grid(True)
      ax.set_xlabel("Number of clusters", fontsize=text_fontsize)
      ax.set_ylabel("Sum of Squared Errors", fontsize=text_fontsize)
      ax.tick_params(labelsize=text_fontsize)
  
      return ax
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import warnings
  import itertools
  import matplotlib.pyplot
  import numpy
  from sklearn.metrics import confusion_matrix
  from sklearn.preprocessing import label_binarize
  from sklearn.metrics import roc_curve
  from sklearn.metrics import auc
  from sklearn.metrics import precision_recall_curve
  from sklearn.metrics import average_precision_score
  from sklearn.utils.multiclass import unique_labels
  from sklearn.model_selection import learning_curve
  from sklearn.base import clone
  from sklearn.metrics import silhouette_score
  from sklearn.metrics import silhouette_samples
  from sklearn.utils import deprecated
  from scipy import interp
  from scikitplot.helpers import binary_ks_curve, validate_labels
label_desc: Use image node_id 8 for calling a global function with example usage: plot_elbow_curve(clf, X, title, cluster_ranges, ax, figsize, title_fontsize, text_fontsize) and returns: ax