code: def register_model(model_class: Type[Model]) -> None:
      """Add a custom model class to the SQA and JSON registries."""
      class_name = model_class.__name__
      CLASS_TO_REGISTRY[Model].update({model_class: class_name})
      CLASS_TO_REVERSE_REGISTRY[Model].update({class_name: model_class})
  
imports: from typing import Any, Dict, Type
  import torch
  from ax.models.torch.botorch_modular.acquisition import Acquisition
  from ax.models.torch.botorch_modular.sebo import SEBOAcquisition
  from botorch.acquisition.acquisition import AcquisitionFunction
  from botorch.acquisition.analytic import ExpectedImprovement, LogExpectedImprovement, LogNoisyExpectedImprovement, NoisyExpectedImprovement
  from botorch.acquisition.knowledge_gradient import qKnowledgeGradient, qMultiFidelityKnowledgeGradient
  from botorch.acquisition.logei import qLogExpectedImprovement, qLogNoisyExpectedImprovement
  from botorch.acquisition.max_value_entropy_search import qMaxValueEntropy, qMultiFidelityMaxValueEntropy
  from botorch.acquisition.monte_carlo import qExpectedImprovement, qNoisyExpectedImprovement
  from botorch.acquisition.multi_objective.logei import qLogExpectedHypervolumeImprovement, qLogNoisyExpectedHypervolumeImprovement
  from botorch.acquisition.multi_objective.monte_carlo import qExpectedHypervolumeImprovement, qNoisyExpectedHypervolumeImprovement
  from botorch.models import SaasFullyBayesianSingleTaskGP
  from botorch.models.fully_bayesian_multitask import SaasFullyBayesianMultiTaskGP
  from botorch.models.gp_regression import FixedNoiseGP, SingleTaskGP
  from botorch.models.gp_regression_fidelity import FixedNoiseMultiFidelityGP, SingleTaskMultiFidelityGP
  from botorch.models.gp_regression_mixed import MixedSingleTaskGP
  from botorch.models.model import Model
  from botorch.models.model_list_gp_regression import ModelListGP
  from botorch.models.multitask import FixedNoiseMultiTaskGP, MultiTaskGP
  from botorch.models.transforms.input import ChainedInputTransform, InputPerturbation, InputTransform, Normalize, Round, Warp
  from botorch.models.transforms.outcome import ChainedOutcomeTransform, OutcomeTransform, Standardize
  from gpytorch.constraints import Interval
  from gpytorch.likelihoods.gaussian_likelihood import GaussianLikelihood
  from gpytorch.likelihoods.likelihood import Likelihood
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from gpytorch.mlls.leave_one_out_pseudo_likelihood import LeaveOneOutPseudoLikelihood
  from gpytorch.mlls.marginal_log_likelihood import MarginalLogLikelihood
  from gpytorch.mlls.sum_marginal_log_likelihood import SumMarginalLogLikelihood
  from gpytorch.priors.torch_priors import GammaPrior
label_desc: Use image node_id 3 for calling a global function with example usage: register_model(model_class) without return types