code: def make_data(n_samples, n_features):
      alpha = 0.4
      cov, prec, adj = LatticeGraph(
          n_blocks=5, chain_blocks=True, random_sign=True, seed=1
      ).create(n_features, alpha)
      prng = np.random.RandomState(2)
      X = prng.multivariate_normal(
          np.zeros(n_features), cov, size=n_samples
      )
      return X, cov, prec
  
file_path: skggm/examples/estimator_suite_spark.py
imports: import sys
  import numpy
  import tabulate
  import time
  import matplotlib.pyplot
  from sklearn.covariance import ledoit_wolf
  from inverse_covariance import QuicGraphicalLasso, QuicGraphicalLassoCV, QuicGraphicalLassoEBIC, AdaptiveGraphicalLasso, ModelAverage
  from inverse_covariance.profiling import LatticeGraph
label_desc: Use image node_id 1 for calling a global function with example usage: make_data(n_samples, n_features) and returns: X,  cov,  prec