class_docstr: Base class for Gaussian processes. 
class_docstr_tok: ["Base","class","for","Gaussian","processes","."]
docstr: Draws samples with hallucinated observations. 
docstr_tok: ["Draws","samples","with","hallucinated","observations","."]
code: def draw_samples_with_hallucinated_observations(
      self, num_samples, X_test, X_halluc
  ):
      """Draws samples with hallucinated observations."""
      mean_vals, aug_covar = self.eval_with_hallucinated_observations(
          X_test, X_halluc, uncert_form="covar"
      )
      return draw_gaussian_samples(num_samples, mean_vals, aug_covar)
  
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 16 for calling the GP obj's underlying member method code with example usage: obj.draw_samples_with_hallucinated_observations(num_samples, X_test, X_halluc) and returns: draw_gaussian_samples