code: def _fully_random_weights(n_features, lam_scale, prng):
      
      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
docs: Generate a symmetric random matrix with zeros along the diagonal.