code: def _fix_weights(weight_fun, *args):
      """Ensure random weight matrix is valid.
  
      TODO:  The diagonally dominant tuning currently doesn't make sense.
             Our weight matrix has zeros along the diagonal, so multiplying by
             a diagonal matrix results in a zero-matrix.
      """
      weights = weight_fun(*args)
  
      # TODO: fix this
      # disable checks for now
      return weights
  
      # if positive semidefinite, then we're good as is
      if _check_psd(weights):
          return weights
  
      # make diagonally dominant
      off_diag_sums = np.sum(
          weights, axis=1
      )  # NOTE: assumes diag is zero
      mod_mat = np.linalg.inv(np.sqrt(np.diag(off_diag_sums)))
      return np.dot(mod_mat, weights, mod_mat)
  
imports: from __future__ import absolute_import
  import numpy
  from sklearn.base import BaseEstimator, clone
  from sklearn.utils import check_array, as_float_array
  from joblib import Parallel, delayed
  from functools import partial
  from .inverse_covariance import _init_coefs
  from .None import QuicGraphicalLasso
label_desc: Use image node_id 4 for calling a global function with example usage: _fix_weights(weight_fun) and returns: weights, np, weights