code: def test_get_parameters_and_bounds(self):
      param_dict, bounds_dict = get_parameters_and_bounds(self.module)
      self.assertEqual(1, len(param_dict))
      self.assertEqual(1, len(bounds_dict))
  
      name, bounds = next(iter(bounds_dict.items()))
      self.assertEqual(name, "noise_covar.raw_noise")
      self.assertEqual(bounds, (-float("inf"), float("inf")))
  
      mock_module = torch.nn.Module()
      mock_module.named_parameters = MagicMock(
          return_value=self.module.named_parameters()
      )
      param_dict2, bounds_dict2 = get_parameters_and_bounds(mock_module)
      self.assertEqual(param_dict, param_dict2)
      self.assertEqual(len(bounds_dict2), 0)
  
imports: from __future__ import annotations
  import re
  import warnings
  from copy import deepcopy
  from string import ascii_lowercase
  from unittest.mock import MagicMock, patch
  import torch
  from botorch import settings
  from botorch.models import SingleTaskGP
  from botorch.optim.utils import get_data_loader, get_name_filter, get_parameters, get_parameters_and_bounds, model_utils, sample_all_priors
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.constraints import GreaterThan
  from gpytorch.kernels.matern_kernel import MaternKernel
  from gpytorch.kernels.scale_kernel import ScaleKernel
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from gpytorch.priors import UniformPrior
  from gpytorch.priors.prior import Prior
  from gpytorch.priors.torch_priors import GammaPrior
label_desc: Use image node_id 3 for calling the TestGetParameters obj's underlying member method code with example usage: obj.test_get_parameters_and_bounds() without return types