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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docstr: Fit the optimizer with new architectures and performances.
  Args:
      x_queue: A list of NetworkDescriptor.
      y_queue: A list of metric values.
docstr_tok: ["Fit","the","optimizer","with","new","architectures","and","performances",".","Args",":","x_queue",":","A","list","of","NetworkDescriptor",".","y_queue",":","A","list","of","metric","values","."]
code: def fit(self, x_queue, y_queue):
      """Fit the optimizer with new architectures and performances.
      Args:
          x_queue: A list of NetworkDescriptor.
          y_queue: A list of metric values.
      """
      self.gpr.fit(x_queue, y_queue)
  
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 2 for calling the BayesianOptimizer obj's underlying member method code with example usage: obj.fit(x_queue, y_queue) without return types