code: def _fully_random_weights(n_features, lam_scale, prng):
      """Generate a symmetric random matrix with zeros along the diagonal."""
      weights = np.zeros((n_features, n_features))
      n_off_diag = int((n_features**2 - n_features) / 2)
      weights[
          np.triu_indices(n_features, k=1)
      ] = 0.1 * lam_scale * prng.randn(n_off_diag) + (0.25 * lam_scale)
      weights[weights < 0] = 0
      weights = weights + weights.T
      return weights
  
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 2 for calling a global function with example usage: _fully_random_weights(n_features, lam_scale, prng) and returns: weights