code: def test_FullyBayesianBotorchModelPyro_cuda_double(self) -> None:
      if torch.cuda.is_available():
          self.test_FullyBayesianBotorchModelPyro(
              dtype=torch.double, cuda=True
          )
  
imports: import dataclasses
  import warnings
  from abc import ABC
  from contextlib import ExitStack
  from itertools import count, product
  from logging import Logger
  from math import sqrt
  from typing import Any, cast, Dict, Type
  from unittest import mock
  import pyro
  import torch
  from ax.core.search_space import SearchSpaceDigest
  from ax.exceptions.core import AxError
  from ax.models.torch.fully_bayesian import FullyBayesianBotorchModel, FullyBayesianMOOBotorchModel, matern_kernel, rbf_kernel, single_task_pyro_model
  from ax.models.torch_base import TorchOptConfig
  from ax.utils.common.constants import Keys
  from ax.utils.common.logger import get_logger
  from ax.utils.common.testutils import TestCase
  from ax.utils.testing.torch_stubs import get_torch_test_data
  from botorch.acquisition.utils import get_infeasible_cost
  from botorch.models import ModelListGP
  from botorch.models.gp_regression import MIN_INFERRED_NOISE_LEVEL
  from botorch.models.model import ModelList
  from botorch.models.transforms.input import Warp
  from botorch.optim.optimize import optimize_acqf
  from botorch.posteriors.gpytorch import GPyTorchPosterior
  from botorch.utils import get_objective_weights_transform
  from botorch.utils.datasets import SupervisedDataset
  from gpytorch.constraints import GreaterThan, Positive
  from gpytorch.kernels import MaternKernel, RBFKernel, ScaleKernel
  from gpytorch.likelihoods import _GaussianLikelihoodBase
  from pyro.infer.mcmc import MCMC, NUTS
  from pyro.ops.integrator import potential_grad
label_desc: Use image node_id 10 for calling the BaseFullyBayesianBotorchModelTest obj's underlying member method code with example usage: obj.test_FullyBayesianBotorchModelPyro_cuda_double() without return types