code: def silhouette(
      clusterer=None,
      X=None,
      cluster_labels=None,
      labels=None,
      metric="euclidean",
      kmeans=True,
  ):
      """Measures & plots silhouette coefficients.
  
      Silhouette coefficients near +1 indicate that the sample is far away from
      the neighboring clusters. A value near 0 indicates that the sample is on or
      very close to the decision boundary between two neighboring clusters and
      negative values indicate that the samples might have been assigned to the wrong cluster.
  
      Should only be called with a fitted clusterer (otherwise an error is thrown).
  
      Please note this function fits the model on the training set when called.
  
      Arguments:
          model: (clusterer) Takes in a fitted clusterer.
          X: (arr) Training set features.
          cluster_labels: (list) Names for cluster labels. Makes plots easier to read
                                 by replacing cluster indexes with corresponding names.
  
      Returns:
          None: To see plots, go to your W&B run page then expand the 'media' tab
                under 'auto visualizations'.
  
      Example:
      ```python
      wandb.sklearn.plot_silhouette(model, X_train, ["spam", "not spam"])
      ```
      """
      not_missing = utils.test_missing(clusterer=clusterer)
      correct_types = utils.test_types(clusterer=clusterer)
      is_fitted = utils.test_fitted(clusterer)
  
      if not_missing and correct_types and is_fitted:
          if isinstance(X, (pd.DataFrame)):
              X = X.values
          silhouette_chart = calculate.silhouette(
              clusterer, X, cluster_labels, labels, metric, kmeans
          )
          wandb.log({"silhouette_plot": silhouette_chart})
  
file_path: wandb/wandb/sklearn/plot/clusterer.py
imports: from warnings import simplefilter
  import pandas
  import sklearn
  import wandb
  from wandb.sklearn import calculate, utils
label_desc: Use image node_id 3 for calling a global function with example usage: silhouette(clusterer, X, cluster_labels, labels, metric, kmeans) without return types