code: def _get_model(
      dtype,
      device,
      multi_output=False,
      use_transforms=False,
      batched_inputs=False,
  ):
      tkwargs = {"dtype": dtype, "device": device}
      train_X = torch.tensor(
          [
              [-0.1000],
              [0.4894],
              [1.0788],
              [1.6681],
              [2.2575],
              [2.8469],
              [3.4363],
              [4.0257],
              [4.6150],
              [5.2044],
              [5.7938],
              [6.3832],
          ],
          **tkwargs,
      )
      train_Y = torch.tensor(
          [
              [-0.0274],
              [0.2612],
              [0.8114],
              [1.1916],
              [1.4870],
              [0.8611],
              [-0.9226],
              [-0.5916],
              [-1.3301],
              [-1.8847],
              [0.0647],
              [1.0900],
          ],
          **tkwargs,
      )
      state_dict = {
          "likelihood.noise_covar.raw_noise": torch.tensor(
              [0.0214], **tkwargs
          ),
          "likelihood.noise_covar.noise_prior.concentration": torch.tensor(
              1.1000, **tkwargs
          ),
          "likelihood.noise_covar.noise_prior.rate": torch.tensor(
              0.0500, **tkwargs
          ),
          "mean_module.raw_constant": torch.tensor(0.1398, **tkwargs),
          "covar_module.raw_outputscale": torch.tensor(
              0.6933, **tkwargs
          ),
          "covar_module.base_kernel.raw_lengthscale": torch.tensor(
              [[-0.0444]], **tkwargs
          ),
          "covar_module.base_kernel.lengthscale_prior.concentration": torch.tensor(
              3.0, **tkwargs
          ),
          "covar_module.base_kernel.lengthscale_prior.rate": torch.tensor(
              6.0, **tkwargs
          ),
          "covar_module.outputscale_prior.concentration": torch.tensor(
              2.0, **tkwargs
          ),
          "covar_module.outputscale_prior.rate": torch.tensor(
              0.1500, **tkwargs
          ),
      }
      if multi_output:
          train_Y2 = torch.tensor(
              [
                  [0.9723],
                  [1.0652],
                  [0.7667],
                  [-0.5542],
                  [-0.6266],
                  [-0.5350],
                  [-0.8854],
                  [-1.3024],
                  [1.0408],
                  [0.2485],
                  [1.4924],
                  [1.5393],
              ],
              **tkwargs,
          )
          train_Y = torch.cat([train_Y, train_Y2], dim=-1)
          state_dict["likelihood.noise_covar.raw_noise"] = torch.stack(
              [
                  state_dict["likelihood.noise_covar.raw_noise"],
                  torch.tensor([0.0745], **tkwargs),
              ]
          )
          state_dict["mean_module.raw_constant"] = torch.stack(
              [
                  state_dict["mean_module.raw_constant"],
                  torch.tensor(0.3276, **tkwargs),
              ]
          )
          state_dict["covar_module.raw_outputscale"] = torch.stack(
              [
                  state_dict["covar_module.raw_outputscale"],
                  torch.tensor(0.4394, **tkwargs),
              ],
              dim=-1,
          )
          state_dict[
              "covar_module.base_kernel.raw_lengthscale"
          ] = torch.stack(
              [
                  state_dict[
                      "covar_module.base_kernel.raw_lengthscale"
                  ],
                  torch.tensor([[-0.4617]], **tkwargs),
              ]
          )
  
      if batched_inputs:
          # both are supported but not included in units.
          assert not (multi_output or use_transforms)
          state_dict["likelihood.noise_covar.raw_noise"] = torch.tensor(
              [[0.0214], [0.001]], **tkwargs
          )
          state_dict["mean_module.raw_constant"] = torch.tensor(
              [0.1398, 0.5], **tkwargs
          )
          state_dict["covar_module.raw_outputscale"] = torch.tensor(
              [0.6933, 1.0], **tkwargs
          )
          state_dict[
              "covar_module.base_kernel.raw_lengthscale"
          ] = torch.tensor([[[-0.0444]], [[5.0]]], **tkwargs)
          train_X = train_X.expand(2, -1, -1)
          train_Y = train_Y.expand(2, -1, -1)
  
      if use_transforms:
          bounds = torch.zeros(2, 1, **tkwargs)
          bounds[1] = 10.0
          intf = Normalize(d=1, bounds=bounds)
          octf = Standardize(m=train_Y.shape[-1])
  
          state_dict["likelihood.noise_covar.raw_noise"] = torch.tensor(
              [[0.1743], [0.3132]] if multi_output else [0.1743],
              **tkwargs,
          )
          state_dict["mean_module.raw_constant"] = torch.tensor(
              [0.2560, 0.6714] if multi_output else 0.2555, **tkwargs
          )
          state_dict["covar_module.raw_outputscale"] = torch.tensor(
              [2.4396, 2.6821] if multi_output else 2.4398, **tkwargs
          )
          state_dict[
              "covar_module.base_kernel.raw_lengthscale"
          ] = torch.tensor(
              [[[-1.6197]], [[-1.0532]]]
              if multi_output
              else [[-1.6198]],
              **tkwargs,
          )
          state_dict["outcome_transform.means"] = torch.tensor(
              [[0.0842, 0.2685]] if multi_output else [[0.0842]],
              **tkwargs,
          )
          state_dict["outcome_transform.stdvs"] = torch.tensor(
              [[1.0757, 1.0005]] if multi_output else [[1.0757]],
              **tkwargs,
          )
          state_dict["outcome_transform._stdvs_sq"] = torch.tensor(
              [[1.1572, 1.0010]] if multi_output else [[1.1572]],
              **tkwargs,
          )
      else:
          intf = None
          octf = None
  
      model = SingleTaskGP(
          train_X, train_Y, outcome_transform=octf, input_transform=intf
      ).eval()
      model.load_state_dict(state_dict, strict=False)
      return model, train_X, train_Y
  
imports: from itertools import product
  from math import pi
  from unittest import mock
  import torch
  from botorch.models.converter import batched_to_model_list
  from botorch.models.deterministic import DeterministicModel
  from botorch.models.fully_bayesian import SaasFullyBayesianSingleTaskGP
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.models.model import ModelList
  from botorch.models.multitask import MultiTaskGP
  from botorch.models.transforms.input import Normalize
  from botorch.models.transforms.outcome import Standardize
  from botorch.utils.gp_sampling import get_deterministic_model, get_deterministic_model_list, get_deterministic_model_multi_samples, get_gp_samples, get_weights_posterior, GPDraw, RandomFourierFeatures
  from botorch.utils.testing import BotorchTestCase
  from botorch.utils.transforms import is_ensemble
  from gpytorch.kernels import MaternKernel, PeriodicKernel, RBFKernel, ScaleKernel
  from torch.distributions import MultivariateNormal
label_desc: Use image node_id 1 for calling a global function with example usage: _get_model(dtype, device, multi_output, use_transforms, batched_inputs) and returns: model,  train_X,  train_Y