code: def __init__(self, train_x, train_y):
      batch_shape = train_x.shape[:-2]
      noise_prior = GammaPrior(1.1, 0.05)
      noise_prior_mode = (
          noise_prior.concentration - 1
      ) / noise_prior.rate
      likelihood = GaussianLikelihood(
          noise_prior=noise_prior,
          batch_shape=batch_shape,
          noise_constraint=GreaterThan(
              1e-4,
              transform=None,
              initial_value=noise_prior_mode,
          ),
      )
      super().__init__(train_x, train_y, likelihood)
      self.mean_module = ConstantMean(batch_shape=batch_shape)
      self.covar_module = ScaleKernel(
          MaternKernel(
              nu=2.5,
              ard_num_dims=train_x.shape[-1],
              batch_shape=batch_shape,
              lengthscale_prior=GammaPrior(3.0, 6.0),
          ),
          batch_shape=batch_shape,
          outputscale_prior=GammaPrior(2.0, 0.15),
      )
  
file_path: gpytorch/test/mlls/test_exact_marginal_log_likelihood.py
imports: import unittest
  import torch
  from gpytorch.constraints.constraints import GreaterThan
  from gpytorch.distributions.multivariate_normal import MultivariateNormal
  from gpytorch.kernels.matern_kernel import MaternKernel
  from gpytorch.kernels.scale_kernel import ScaleKernel
  from gpytorch.likelihoods.gaussian_likelihood import GaussianLikelihood
  from gpytorch.means.constant_mean import ConstantMean
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from gpytorch.models.exact_gp import ExactGP
  from gpytorch.priors.torch_priors import GammaPrior
label_desc: Use image node_id 1 to create a new ExactGPModel object from inherited base classes: ExactGP with example: obj = ExactGPModel(train_x, train_y)