code: def __init__(self, train_inputs, train_targets, likelihood):
      super(ExactGPModel, self).__init__(
          train_inputs, train_targets, likelihood
      )
      self.mean_module = ConstantMean(
          constant_prior=SmoothedBoxPrior(-1, 1)
      )
      self.covar_module = ScaleKernel(
          RBFKernel(
              lengthscale_prior=SmoothedBoxPrior(
                  exp(-3), exp(3), sigma=0.1
              )
          )
      )
  
imports: import unittest
  from math import exp, pi
  import torch
  from torch import optim
  import gpytorch
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.kernels import RBFKernel, ScaleKernel
  from gpytorch.likelihoods import FixedNoiseGaussianLikelihood
  from gpytorch.likelihoods.noise_models import FixedGaussianNoise
  from gpytorch.means import ConstantMean
  from gpytorch.priors import SmoothedBoxPrior
  from gpytorch.test.base_test_case import BaseTestCase
  from gpytorch.test.utils import least_used_cuda_device
label_desc: Use image node_id 1 to create a new ExactGPModel object from inherited base classes: gpytorch.models with example: obj = ExactGPModel(train_inputs, train_targets, likelihood)