code: def make_table(
      x,
      y,
      colors,
      centerx,
      centery,
      y_sil,
      x_sil,
      color_sil,
      silhouette_avg,
  ):
      columns = [
          "x",
          "y",
          "colors",
          "centerx",
          "centery",
          "y_sil",
          "x1",
          "x2",
          "color_sil",
          "silhouette_avg",
      ]
  
      data = [
          [
              x[i],
              y[i],
              colors[i],
              centerx[colors[i]],
              centery[colors[i]],
              y_sil[i],
              0,
              x_sil[i],
              color_sil[i],
              silhouette_avg,
          ]
          for i in range(len(color_sil))
      ]
  
      table = wandb.Table(data=data, columns=columns)
  
      return table
  
file_path: wandb/wandb/sklearn/calculate/silhouette.py
imports: from warnings import simplefilter
  import numpy
  from sklearn.metrics import silhouette_samples, silhouette_score
  from sklearn.preprocessing import LabelEncoder
  import wandb
  from wandb.sklearn import utils
label_desc: Use image node_id 2 for calling a global function with example usage: make_table(x, y, colors, centerx, centery, y_sil, x_sil, color_sil, silhouette_avg) and returns: table