code: def get_and_fit_model(
      Xs: List[Tensor],
      Ys: List[Tensor],
      Yvars: List[Tensor],
      task_features: List[int],
      fidelity_features: List[int],
      metric_names: List[str],
      state_dict: Optional[Dict[str, Tensor]] = None,
      refit_model: bool = True,
      use_input_warping: bool = False,
      use_loocv_pseudo_likelihood: bool = False,
      prior: Optional[Dict[str, Any]] = None,
      *,
      multitask_gp_ranks: Optional[
          Dict[str, Union[Prior, float]]
      ] = None,
      **kwargs: Any,
  ) -> GPyTorchModel:
      r"""Instantiates and fits a botorch GPyTorchModel using the given data.
      N.B. Currently, the logic for choosing ModelListGP vs other models is handled
      using if-else statements in lines 96-137. In the future, this logic should be
      taken care of by modular botorch.
  
      Args:
          Xs: List of X data, one tensor per outcome.
          Ys: List of Y data, one tensor per outcome.
          Yvars: List of observed variance of Ys.
          task_features: List of columns of X that are tasks.
          fidelity_features: List of columns of X that are fidelity parameters.
          metric_names: Names of each outcome Y in Ys.
          state_dict: If provided, will set model parameters to this state
              dictionary. Otherwise, will fit the model.
          refit_model: Flag for refitting model.
          prior: Optional[Dict]. A dictionary that contains the specification of
              GP model prior. Currently, the keys include:
              - covar_module_prior: prior on covariance matrix e.g.
                  {"lengthscale_prior": GammaPrior(3.0, 6.0)}.
              - type: type of prior on task covariance matrix e.g.`LKJCovariancePrior`.
              - sd_prior: A scalar prior over nonnegative numbers, which is used for the
                  default LKJCovariancePrior task_covar_prior.
              - eta: The eta parameter on the default LKJ task_covar_prior.
          kwargs: Passed to `_get_model`.
  
      Returns:
          A fitted GPyTorchModel.
      """
  
      if len(fidelity_features) > 0 and len(task_features) > 0:
          raise NotImplementedError(
              "Currently do not support MF-GP models with task_features!"
          )
      if len(fidelity_features) > 1:
          raise NotImplementedError(
              "Fidelity MF-GP models currently support only a single fidelity parameter!"
          )
      if len(task_features) > 1:
          raise NotImplementedError(
              f"This model only supports 1 task feature (got {task_features})"
          )
      elif len(task_features) == 1:
          task_feature = task_features[0]
      else:
          task_feature = None
  
      model = _construct_model(
          task_feature=task_feature,
          Xs=Xs,
          Ys=Ys,
          Yvars=Yvars,
          fidelity_features=fidelity_features,
          metric_names=metric_names,
          use_input_warping=use_input_warping,
          prior=prior,
          multitask_gp_ranks=multitask_gp_ranks,
          **kwargs,
      )
  
      # TODO: Better logic for deciding when to use a ModelListGP. Currently the
      # logic is unclear. The two cases in which ModelListGP is used are
      # (i) the training inputs (Xs) are not the same for the different outcomes, and
      # (ii) a multi-task model is used
  
      model.to(Xs[0])
      if state_dict is not None:
          model.load_state_dict(state_dict)
      if state_dict is None or refit_model:
          # TODO: Add bounds for optimization stability - requires revamp upstream
          bounds = {}
          if use_loocv_pseudo_likelihood:
              mll_cls = LeaveOneOutPseudoLikelihood
          else:
              mll_cls = ExactMarginalLogLikelihood
          if isinstance(model, ModelListGP):
              mll = SumMarginalLogLikelihood(
                  model.likelihood, model, mll_cls=mll_cls
              )
          else:
              mll = mll_cls(model.likelihood, model)
          mll = fit_gpytorch_mll(
              mll, optimizer_kwargs={"bounds": bounds}
          )
      return model
  
imports: import functools
  from copy import deepcopy
  from random import randint
  from typing import Any, Callable, Dict, List, Optional, Protocol, Tuple, Type, Union
  import torch
  from ax.models.model_utils import best_observed_point, get_observed
  from ax.models.torch.utils import _to_inequality_constraints
  from ax.models.torch_base import TorchModel
  from ax.models.types import TConfig
  from botorch.acquisition import get_acquisition_function
  from botorch.acquisition.acquisition import AcquisitionFunction
  from botorch.acquisition.fixed_feature import FixedFeatureAcquisitionFunction
  from botorch.acquisition.objective import ConstrainedMCObjective, GenericMCObjective
  from botorch.acquisition.utils import get_infeasible_cost
  from botorch.exceptions.errors import UnsupportedError
  from botorch.fit import fit_gpytorch_mll
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.models.gp_regression_fidelity import SingleTaskMultiFidelityGP
  from botorch.models.gpytorch import GPyTorchModel
  from botorch.models.model import Model
  from botorch.models.model_list_gp_regression import ModelListGP
  from botorch.models.multitask import MultiTaskGP
  from botorch.models.transforms.input import Warp
  from botorch.optim.optimize import optimize_acqf
  from botorch.utils import get_objective_weights_transform, get_outcome_constraint_transforms
  from botorch.utils.multi_objective.scalarization import get_chebyshev_scalarization
  from botorch.utils.transforms import is_ensemble
  from gpytorch.kernels import MaternKernel, ScaleKernel
  from gpytorch.kernels.kernel import Kernel
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from gpytorch.mlls.leave_one_out_pseudo_likelihood import LeaveOneOutPseudoLikelihood
  from gpytorch.mlls.sum_marginal_log_likelihood import SumMarginalLogLikelihood
  from gpytorch.priors import Prior
  from gpytorch.priors.lkj_prior import LKJCovariancePrior
  from gpytorch.priors.torch_priors import GammaPrior, LogNormalPrior
  from torch import Tensor
label_desc: Use image node_id 2 for calling a global function with example usage: get_and_fit_model(Xs, Ys, Yvars, task_features, fidelity_features, metric_names, state_dict, refit_model, use_input_warping, use_loocv_pseudo_likelihood, prior) and returns: model