class_docstr: A Bayesian optimizer for neural architectures.
  Attributes:
      searcher: The Searcher which is calling the Bayesian optimizer.
      t_min: The minimum temperature for simulated annealing.
      metric: An instance of the Metric subclasses.
      gpr: A GaussianProcessRegressor for bayesian optimization.
      beta: The beta in acquisition function. (refer to our paper)
      search_tree: The network morphism search tree.
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code: def _accept_new_acq_value(self, opt_acq, temp_acq_value):
      if (
          temp_acq_value > opt_acq
          and self.optimizemode is OptimizeMode.Maximize
      ):
          return True
      if (
          temp_acq_value < opt_acq
          and not self.optimizemode is OptimizeMode.Maximize
      ):
          return True
      return False
  
imports: import math
  import random
  from copy import deepcopy
  from functools import total_ordering
  from queue import PriorityQueue
  import numpy
  from scipy.linalg import LinAlgError, cho_solve, cholesky, solve_triangular
  from scipy.optimize import linear_sum_assignment
  from sklearn.metrics.pairwise import rbf_kernel
  from nni.utils import OptimizeMode
  from .graph_transformer import transform
  from .utils import Constant
  from .layers import is_layer
label_desc: Use image node_id 6 for calling the BayesianOptimizer obj's underlying member method code with example usage: obj._accept_new_acq_value(opt_acq, temp_acq_value) and returns: False, True, True