class_docstr: Class for fitting Gaussian processes. 
class_docstr_tok: ["Class","for","fitting","Gaussian","processes","."]
docstr: Updates hp_tune method weight in the case of adaptive tuning. 
docstr_tok: ["Updates","hp_tune","method","weight","in","the","case","of","adaptive","tuning","."]
code: def update_hp_tune_method_weight(self, method, weight_to_add=1):
      """Updates hp_tune method weight in the case of adaptive tuning."""
      if self.options.hp_tune_probs == "adaptive":
          self.hp_tune_sampling_weights[method] += weight_to_add
  
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 18 for calling the GPFitter obj's underlying member method code with example usage: obj.update_hp_tune_method_weight(method, weight_to_add) without return types