code: def silhouette_analysis(
      X,
      clf,
      range_n_clusters=None,
      metric="euclidean",
      figsize=None,
      cmap="nipy_spectral",
      text_fontsize="medium",
      ax=None,
  ):
      """Plots silhouette analysis of clusters provided.
  
      Parameters
      -----------
  
      X : array-like, shape = [n_samples, n_features]:
          Cluster data, where n_samples is the number of samples and
          n_features is the number of features.
          Refer https://numpy.org/doc/stable/glossary.html#term-array-like
  
      clf
          Clusterer instance that implements ``fit``,``fit_predict``, and
          ``score`` methods, and an ``n_clusters`` hyperparameter.
          e.g. :class:`sklearn.cluster.KMeans` instance
  
      range_n_clusters : None or :obj:`list` of int, optional
          List of n_clusters for which to plot the silhouette scores.
          Defaults to ``[2, 3, 4, 5, 6]``.
  
      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.
  
      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
  
      text_fontsize : string or int, optional:
          Matplotlib-style fontsizes.
          Use e.g. "small", "medium", "large" or integer-values. Defaults to
          "medium".
  
      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.
  
      Returns
      -------
      ax: matplotlib Axes
          Axes containing the plot
  
  
      Examples
      --------
      .. plot:: ../examples/silhouette_plot_basic.py
  
      Notes
      -----
      .. versionadded:: 0.8.3
  
      """
      if range_n_clusters is None:
          range_n_clusters = [2, 3, 4, 5, 6]
      else:
          range_n_clusters = sorted(range_n_clusters)
  
      if not hasattr(clf, "n_clusters"):
          raise TypeError(
              '"n_clusters" attribute not in classifier. '
              "Cannot plot silhouette analysis ."
          )
  
      for n_clusters in range_n_clusters:
          _, ax = plt.subplots(1, 1, figsize=figsize)
          clf = clone(clf)
          setattr(clf, "n_clusters", n_clusters)
          cluster_labels = clf.fit_predict(X)
  
          ax = silhouette_analysis_from_results(
              X,
              cluster_labels,
              metric,
              figsize,
              cmap,
              text_fontsize,
              ax,
          )
      return ax
  
file_path: sklearn-evaluation/src/sklearn_evaluation/plot/clustering.py
imports: import time
  import matplotlib.pyplot
  import numpy
  from sklearn.base import clone
  from sklearn.metrics import silhouette_samples, silhouette_score
  from sklearn.preprocessing import LabelEncoder
  from joblib import Parallel, delayed
  from sklearn_evaluation.telemetry import SKLearnEvaluationLogger
  from ploomber_core.exceptions import modify_exceptions
  from ploomber_core import deprecated
label_desc: Use image node_id 4 for calling a global function with example usage: silhouette_analysis(X, clf, range_n_clusters, metric, figsize, cmap, text_fontsize, ax) and returns: ax