code: def get_MOO_RS(
      experiment: Experiment,
      data: Data,
      search_space: Optional[SearchSpace] = None,
      dtype: torch.dtype = torch.double,
      device: torch.device = DEFAULT_TORCH_DEVICE,
      optimization_config: Optional[OptimizationConfig] = None,
  ) -> TorchModelBridge:
      """Instantiates a Random Scalarization multi-objective model.
  
      Chooses a different random linear scalarization of the objectives
      for generating each new candidate arm. This will only explore the
      convex hull of the pareto frontier.
      """
      check_has_multi_objective_and_data(
          experiment=experiment,
          data=data,
          optimization_config=optimization_config,
      )
      return checked_cast(
          TorchModelBridge,
          Models.MOO(
              experiment=experiment,
              data=data,
              search_space=search_space or experiment.search_space,
              torch_dtype=dtype,
              torch_device=device,
              acqf_constructor=get_qLogNEI,
              default_model_gen_options={
                  "acquisition_function_kwargs": {
                      "random_scalarization": True,
                  }
              },
              optimization_config=optimization_config,
          ),
      )
  
imports: from logging import Logger
  from typing import Any, Dict, List, Optional, Type
  import torch
  from ax.core.data import Data
  from ax.core.experiment import Experiment
  from ax.core.multi_type_experiment import MultiTypeExperiment
  from ax.core.observation import ObservationFeatures
  from ax.core.optimization_config import OptimizationConfig
  from ax.core.search_space import SearchSpace
  from ax.modelbridge.discrete import DiscreteModelBridge
  from ax.modelbridge.modelbridge_utils import check_has_multi_objective_and_data
  from ax.modelbridge.random import RandomModelBridge
  from ax.modelbridge.registry import Cont_X_trans, Models, MT_MTGP_trans, ST_MTGP_trans, Y_trans
  from ax.modelbridge.torch import TorchModelBridge
  from ax.modelbridge.transforms.base import Transform
  from ax.modelbridge.transforms.convert_metric_names import tconfig_from_mt_experiment
  from ax.models.torch.botorch import BotorchModel, TAcqfConstructor, TModelConstructor, TModelPredictor, TOptimizer
  from ax.models.torch.botorch_defaults import get_and_fit_model, get_qLogNEI, scipy_optimizer
  from ax.models.torch.botorch_moo_defaults import get_EHVI
  from ax.models.torch.utils import predict_from_model
  from ax.models.types import TConfig
  from ax.utils.common.logger import get_logger
  from ax.utils.common.typeutils import checked_cast
label_desc: Use image node_id 15 for calling a global function with example usage: get_MOO_RS(experiment, data, search_space, dtype, device, optimization_config) and returns: checked_cast