class_docstr: Gaussian process regressor.
  Attributes:
      alpha: A hyperparameter.
class_docstr_tok: ["Gaussian","process","regressor",".","Attributes",":","alpha",":","A","hyperparameter","."]
docstr: Fit the regressor with more data.
  Args:
      train_x: A list of NetworkDescriptor.
      train_y: A list of metric values.
docstr_tok: ["Fit","the","regressor","with","more","data",".","Args",":","train_x",":","A","list","of","NetworkDescriptor",".","train_y",":","A","list","of","metric","values","."]
code: def fit(self, train_x, train_y):
      """Fit the regressor with more data.
      Args:
          train_x: A list of NetworkDescriptor.
          train_y: A list of metric values.
      """
      if self.first_fitted:
          self.incremental_fit(train_x, train_y)
      else:
          self.first_fit(train_x, train_y)
  
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 3 for calling the IncrementalGaussianProcess obj's underlying member method code with example usage: obj.fit(train_x, train_y) without return types