code: def clustering(fname="clustering.png"):
      # Create side-by-side axes grid
      _, axes = plt.subplots(ncols=2, figsize=(18, 6))
      X, y = make_blobs(centers=7)
  
      # Add K-Elbow to the left
      oz = KElbowVisualizer(MiniBatchKMeans(), k=(3, 12), ax=axes[0])
      oz.fit(X, y)
      oz.finalize()
  
      # Add SilhouetteVisualizer to the right
      oz = SilhouetteVisualizer(Birch(n_clusters=5), ax=axes[1])
      oz.fit(X, y)
      oz.finalize()
  
      # Save figure
      path = os.path.join(FIGURES, fname)
      plt.tight_layout()
      plt.savefig(path)
  
imports: import os
  import argparse
  import numpy
  import pandas
  import matplotlib.pyplot
  from yellowbrick.features import Rank2D, RadViz
  from yellowbrick.model_selection import LearningCurve
  from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer
  from yellowbrick.classifier import ClassificationReport, DiscriminationThreshold
  from yellowbrick.regressor import ResidualsPlot, PredictionError, AlphaSelection
  from collections import namedtuple
  from sklearn.datasets import make_blobs
  from sklearn.naive_bayes import MultinomialNB
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.cluster import MiniBatchKMeans, Birch
  from sklearn.model_selection import train_test_split
  from sklearn.linear_model import LassoCV, RidgeCV, LogisticRegression
label_desc: Use image node_id 8 for calling a global function with example usage: clustering(fname) without return types