code: def get_sobol(
      search_space: SearchSpace,
      seed: Optional[int] = None,
      deduplicate: bool = False,
      init_position: int = 0,
      scramble: bool = True,
      fallback_to_sample_polytope: bool = False,
  ) -> RandomModelBridge:
      """Instantiates a Sobol sequence quasi-random generator.
  
      Args:
          search_space: Sobol generator search space.
          kwargs: Custom args for sobol generator.
  
      Returns:
          RandomModelBridge, with SobolGenerator as model.
      """
      return checked_cast(
          RandomModelBridge,
          Models.SOBOL(
              search_space=search_space,
              seed=seed,
              deduplicate=deduplicate,
              init_position=init_position,
              scramble=scramble,
              fallback_to_sample_polytope=fallback_to_sample_polytope,
          ),
      )
  
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 3 for calling a global function with example usage: get_sobol(search_space, seed, deduplicate, init_position, scramble, fallback_to_sample_polytope) and returns: checked_cast