code: def _compute_results_parallel(n_jobs, clusterer, X, cluster_ranges):
      parallel_runner = Parallel(n_jobs=n_jobs)
      _cluster_scorer = delayed(_clone_and_score_clusterer)
      results = parallel_runner(
          _cluster_scorer(clusterer, X, i) for i in cluster_ranges
      )
  
      clfs, times = zip(*results)
  
      return clfs, times
  
file_path: wandb/wandb/sklearn/calculate/elbow_curve.py
imports: import time
  from warnings import simplefilter
  import numpy
  from joblib import Parallel, delayed
  from sklearn.base import clone
  import wandb
label_desc: Use image node_id 3 for calling a global function with example usage: _compute_results_parallel(n_jobs, clusterer, X, cluster_ranges) and returns: clfs,  times