code: def __init__(self, train_x, train_y, likelihood):
      super(GPRegressionModel, self).__init__(
          train_x, train_y, likelihood
      )
      self.mean_module = ConstantMean(
          constant_prior=SmoothedBoxPrior(-1e-5, 1e-5)
      )
      self.base_covar_module = ScaleKernel(
          RBFKernel(
              lengthscale_prior=SmoothedBoxPrior(
                  exp(-5), exp(6), sigma=0.1
              )
          )
      )
      self.grid_covar_module = GridInterpolationKernel(
          self.base_covar_module, grid_size=50, num_dims=1
      )
      self.covar_module = self.grid_covar_module
  
imports: import os
  import random
  import unittest
  import warnings
  from math import exp, pi
  import torch
  from torch import optim
  import gpytorch
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.kernels import GridInterpolationKernel, RBFKernel, ScaleKernel
  from gpytorch.likelihoods import FixedNoiseGaussianLikelihood
  from gpytorch.means import ConstantMean
  from gpytorch.priors import SmoothedBoxPrior
  from gpytorch.test.utils import least_used_cuda_device
  from gpytorch.utils.warnings import GPInputWarning
label_desc: Use image node_id 1 to create a new GPRegressionModel object from inherited base classes: gpytorch.models with example: obj = GPRegressionModel(train_x, train_y, likelihood)