class_docstr: Sparse inverse covariance estimation with an l1-penalized estimator.
  
  Read more in the :ref:`User Guide <sparse_inverse_covariance>`.
  
  Parameters
  ----------
  alpha : positive float, default 0.01
      The regularization parameter: the higher alpha, the more
      regularization, the sparser the inverse covariance.
  
  mode : {'cd', 'lars'}, default 'cd'
      The Lasso solver to use: coordinate descent or LARS. Use LARS for
      very sparse underlying graphs, where p > n. Elsewhere prefer cd
      which is more numerically stable.
  
  tol : positive float, default 1e-4
      The tolerance to declare convergence: if the dual gap goes below
      this value, iterations are stopped.
  
  enet_tol : positive float, optional
      The tolerance for the elastic net solver used to calculate the descent
      direction. This parameter controls the accuracy of the search direction
      for a given column update, not of the overall parameter estimate. Only
      used for mode='cd'.
  
  max_iter : integer, default 100
      The maximum number of iterations.
  
  verbose : boolean, default False
      If verbose is True, the objective function and dual gap are
      plotted at each iteration.
  
  assume_centered : boolean, default False
      If True, data are not centered before computation.
      Useful when working with data whose mean is almost, but not exactly
      zero.
      If False, data are centered before computation.
  
  Attributes
  ----------
  covariance_ : array-like, shape (n_features, n_features)
      Estimated covariance matrix
  
  precision_ : array-like, shape (n_features, n_features)
      Estimated pseudo inverse matrix.
  
  n_iter_ : int
      Number of iterations run.
  
  See Also
  --------
  graph_lasso, GraphLassoCV
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code: def __init__(
      self,
      alpha=0.01,
      mode="cd",
      tol=1e-4,
      enet_tol=1e-4,
      max_iter=100,
      verbose=False,
      assume_centered=False,
  ):
      self.alpha = alpha
      self.mode = mode
      self.tol = tol
      self.enet_tol = enet_tol
      self.max_iter = max_iter
      self.verbose = verbose
      self.assume_centered = assume_centered
      # The base class needs this for the score method
      self.store_precision = True
  
imports: import warnings
  import operator
  import sys
  import time
  import numpy
  from scipy import linalg
  from .empirical_covariance_ import empirical_covariance, EmpiricalCovariance, log_likelihood
  from ..exceptions import ConvergenceWarning
  from ..utils.extmath import pinvh
  from ..utils.validation import check_random_state, check_array
  from ..linear_model import lars_path
  from ..linear_model import cd_fast
  from ..model_selection import check_cv, cross_val_score
  from ..externals.joblib import Parallel, delayed
  import collections
label_desc: Use image node_id 1 to create a new GraphLasso object from inherited base classes: EmpiricalCovariance with example: obj = GraphLasso(alpha, mode, tol, enet_tol, max_iter, verbose, assume_centered)