code: def alpha_max(emp_cov):
      """Find the maximum alpha for which there are some non-zeros off-diagonal.
  
      Parameters
      ----------
      emp_cov : 2D array, (n_features, n_features)
          The sample covariance matrix
  
      Notes
      -----
  
      This results from the bound for the all the Lasso that are solved
      in GraphLasso: each time, the row of cov corresponds to Xy. As the
      bound for alpha is given by `max(abs(Xy))`, the result follows.
  
      """
      A = np.copy(emp_cov)
      A.flat[:: A.shape[0] + 1] = 0
      return np.max(np.abs(A))
  
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 3 for calling a global function with example usage: alpha_max(emp_cov) and returns: np