code: def compute_bench(alpha, n_samples, n_features, precompute):
      lasso_results = []
      lars_lasso_results = []
  
      it = 0
  
      for ns in n_samples:
          for nf in n_features:
              it += 1
              print("==================")
              print(
                  "Iteration %s of %s"
                  % (it, max(len(n_samples), len(n_features)))
              )
              print("==================")
              n_informative = nf // 10
              X, Y, coef_ = make_regression(
                  n_samples=ns,
                  n_features=nf,
                  n_informative=n_informative,
                  noise=0.1,
                  coef=True,
              )
  
              X /= np.sqrt(np.sum(X**2, axis=0))  # Normalize data
  
              gc.collect()
              print("- benchmarking Lasso")
              clf = Lasso(
                  alpha=alpha,
                  fit_intercept=False,
                  precompute=precompute,
              )
              tstart = time()
              clf.fit(X, Y)
              lasso_results.append(time() - tstart)
  
              gc.collect()
              print("- benchmarking LassoLars")
              clf = LassoLars(
                  alpha=alpha,
                  fit_intercept=False,
                  precompute=precompute,
              )
              tstart = time()
              clf.fit(X, Y)
              lars_lasso_results.append(time() - tstart)
  
      return lasso_results, lars_lasso_results
  
imports: import gc
  from time import time
  import numpy
  from sklearn.datasets import make_regression
label_desc: Use image node_id 1 for calling a global function with example usage: compute_bench(alpha, n_samples, n_features, precompute) and returns: lasso_results,  lars_lasso_results