class_docstr: Gaussian process regressor.
  Attributes:
      alpha: A hyperparameter.
class_docstr_tok: ["Gaussian","process","regressor",".","Attributes",":","alpha",":","A","hyperparameter","."]
docstr: Predict the result.
  Args:
      train_x: A list of NetworkDescriptor.
  Returns:
      y_mean: The predicted mean.
      y_std: The predicted standard deviation.
docstr_tok: ["Predict","the","result",".","Args",":","train_x",":","A","list","of","NetworkDescriptor",".","Returns",":","y_mean",":","The","predicted","mean",".","y_std",":","The","predicted","standard","deviation","."]
code: def predict(self, train_x):
      """Predict the result.
      Args:
          train_x: A list of NetworkDescriptor.
      Returns:
          y_mean: The predicted mean.
          y_std: The predicted standard deviation.
      """
      k_trans = np.exp(
          -np.power(edit_distance_matrix(train_x, self._x), 2)
      )
      y_mean = k_trans.dot(
          self._alpha_vector
      )  # Line 4 (y_mean = f_star)
  
      # compute inverse K_inv of K based on its Cholesky
      # decomposition L and its inverse L_inv
      l_inv = solve_triangular(
          self._l_matrix.T, np.eye(self._l_matrix.shape[0])
      )
      k_inv = l_inv.dot(l_inv.T)
      # Compute variance of predictive distribution
      y_var = np.ones(len(train_x), dtype=float)
      y_var -= np.einsum("ij,ij->i", np.dot(k_trans, k_inv), k_trans)
  
      # Check if any of the variances is negative because of
      # numerical issues. If yes: set the variance to 0.
      y_var_negative = y_var < 0
      if np.any(y_var_negative):
          y_var[y_var_negative] = 0.0
      return y_mean, np.sqrt(y_var)
  
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 7 for calling the IncrementalGaussianProcess obj's underlying member method code with example usage: obj.predict(train_x) and returns: y_mean,  np