class_docstr: Class for fitting Gaussian processes. 
class_docstr_tok: ["Class","for","fitting","Gaussian","processes","."]
docstr: Sets up using the posterior mean for tuning the parameters of the GP. 
docstr_tok: ["Sets","up","using","the","posterior","mean","for","tuning","the","parameters","of","the","GP","."]
code: def _set_up_post_mean_hp_tune(self):
      """Sets up using the posterior mean for tuning the parameters of the GP."""
      raise NotImplementedError("Not implemented post_mean yet.")
  
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 9 for calling the GPFitter obj's underlying member method code with example usage: obj._set_up_post_mean_hp_tune() without return types