code: def make_data(n_samples, n_features):
      prng = np.random.RandomState(1)
      prec = make_sparse_spd_matrix(
          n_features,
          alpha=0.98,
          smallest_coef=0.4,
          largest_coef=0.7,
          random_state=prng,
      )
      cov = np.linalg.inv(prec)
      d = np.sqrt(np.diag(cov))
      cov /= d
      cov /= d[:, np.newaxis]
      prec *= d
      prec *= d[:, np.newaxis]
      X = prng.multivariate_normal(
          np.zeros(n_features), cov, size=n_samples
      )
      X -= X.mean(axis=0)
      X /= X.std(axis=0)
      return X, cov, prec
  
imports: import sys
  import numpy
  import tabulate
  import time
  from sklearn.model_selection import GridSearchCV
  from sklearn.datasets import make_sparse_spd_matrix
  from sklearn.covariance import GraphicalLassoCV, ledoit_wolf
  import matplotlib.pyplot
  from inverse_covariance import QuicGraphicalLasso, QuicGraphicalLassoCV, QuicGraphicalLassoEBIC, AdaptiveGraphicalLasso, ModelAverage
label_desc: Use image node_id 2 for calling a global function with example usage: make_data(n_samples, n_features) and returns: X,  cov,  prec