code: def get_post_covar_from_raw_covar(
      raw_post_covar, noise_var, is_guaranteed_psd
  ):
      """Computes the posterior covariance from the raw_post_covar. This is mostly to
      account for the fact that the kernel may not be psd.
      """
      if is_guaranteed_psd:
          return raw_post_covar
      else:
          epsilon = 0.05 * noise_var
          return project_symmetric_to_psd_cone(
              raw_post_covar, epsilon=epsilon
          )
  
imports: from __future__ import division
  from argparse import Namespace
  from itertools import product
  import sys
  import numpy
  from ..distributions.model import Model
  from ..distributions import continuous
  from ..distributions import discrete
  from ..utils.general_utils import stable_cholesky, draw_gaussian_samples, project_symmetric_to_psd_cone, solve_lower_triangular, solve_upper_triangular
  from ..utils.oper_utils import direct_ft_maximise, pdoo_maximise, random_maximise, random_sample_cts_dscr
  from ..utils.option_handler import get_option_specs, load_options
  from ..utils.reporters import get_reporter
label_desc: Use image node_id 4 for calling a global function with example usage: get_post_covar_from_raw_covar(raw_post_covar, noise_var, is_guaranteed_psd) and returns: raw_post_covar, project_symmetric_to_psd_cone