code: def plot_silhouette(
      X,
      cluster_labels,
      title="Silhouette Analysis",
      metric="euclidean",
      copy=True,
      ax=None,
      figsize=None,
      cmap="nipy_spectral",
      title_fontsize="large",
      text_fontsize="medium",
  ):
      """Plots silhouette analysis of clusters provided.
  
      Args:
          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.
  
          cluster_labels (array-like, shape (n_samples,)):
              Cluster label for each sample.
  
          title (string, optional): Title of the generated plot. Defaults to
              "Silhouette Analysis"
  
          metric (string or callable, optional): The metric to use when
              calculating distance between instances in a feature array.
              If metric is a string, it must be one of the options allowed by
              sklearn.metrics.pairwise.pairwise_distances. If X is
              the distance array itself, use "precomputed" as the metric.
  
          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``.
  
          cmap (string or :class:`matplotlib.colors.Colormap` instance, optional):
              Colormap used for plotting the projection. View Matplotlib Colormap
              documentation for available options.
              https://matplotlib.org/users/colormaps.html
  
          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 as skplt
          >>> kmeans = KMeans(n_clusters=4, random_state=1)
          >>> cluster_labels = kmeans.fit_predict(X)
          >>> skplt.metrics.plot_silhouette(X, cluster_labels)
          <matplotlib.axes._subplots.AxesSubplot object at 0x7fe967d64490>
          >>> plt.show()
  
          .. image:: _static/examples/plot_silhouette.png
             :align: center
             :alt: Silhouette Plot
      """
      cluster_labels = np.asarray(cluster_labels)
  
      le = LabelEncoder()
      cluster_labels_encoded = le.fit_transform(cluster_labels)
  
      n_clusters = len(np.unique(cluster_labels))
  
      silhouette_avg = silhouette_score(
          X, cluster_labels, metric=metric
      )
  
      sample_silhouette_values = silhouette_samples(
          X, cluster_labels, metric=metric
      )
  
      if ax is None:
          fig, ax = plt.subplots(1, 1, figsize=figsize)
  
      ax.set_title(title, fontsize=title_fontsize)
      ax.set_xlim([-0.1, 1])
  
      ax.set_ylim([0, len(X) + (n_clusters + 1) * 10 + 10])
  
      ax.set_xlabel(
          "Silhouette coefficient values", fontsize=text_fontsize
      )
      ax.set_ylabel("Cluster label", fontsize=text_fontsize)
  
      y_lower = 10
  
      for i in range(n_clusters):
          ith_cluster_silhouette_values = sample_silhouette_values[
              cluster_labels_encoded == i
          ]
  
          ith_cluster_silhouette_values.sort()
  
          size_cluster_i = ith_cluster_silhouette_values.shape[0]
          y_upper = y_lower + size_cluster_i
  
          color = plt.cm.get_cmap(cmap)(float(i) / n_clusters)
  
          ax.fill_betweenx(
              np.arange(y_lower, y_upper),
              0,
              ith_cluster_silhouette_values,
              facecolor=color,
              edgecolor=color,
              alpha=0.7,
          )
  
          ax.text(
              -0.05,
              y_lower + 0.5 * size_cluster_i,
              str(le.classes_[i]),
              fontsize=text_fontsize,
          )
  
          y_lower = y_upper + 10
  
      ax.axvline(
          x=silhouette_avg,
          color="red",
          linestyle="--",
          label="Silhouette score: {0:0.3f}".format(silhouette_avg),
      )
  
      ax.set_yticks([])  # Clear the y-axis labels / ticks
      ax.set_xticks(np.arange(-0.1, 1.0, 0.2))
  
      ax.tick_params(labelsize=text_fontsize)
      ax.legend(loc="best", fontsize=text_fontsize)
  
      return ax
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import itertools
  import matplotlib.pyplot
  import numpy
  from sklearn.metrics import confusion_matrix
  from sklearn.preprocessing import label_binarize
  from sklearn.preprocessing import LabelEncoder
  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.metrics import silhouette_score
  from sklearn.metrics import silhouette_samples
  from sklearn.calibration import calibration_curve
  from sklearn.utils import deprecated
  from scipy import interp
  from scikitplot.helpers import binary_ks_curve, validate_labels
  from scikitplot.helpers import cumulative_gain_curve
label_desc: Use image node_id 7 for calling a global function with example usage: plot_silhouette(X, cluster_labels, title, metric, copy, ax, figsize, cmap, title_fontsize, text_fontsize) and returns: ax