code: def get_tensor_converter_model(
      experiment: Experiment, data: Data
  ) -> TorchModelBridge:
      """
      Constructs a minimal model for converting things to tensors.
  
      Model fitting will instantiate all of the transforms but will not do any
      expensive (i.e. GP) fitting beyond that. The model will raise an error if
      it is used for predicting or generating.
  
      Will work for any search space regardless of types of parameters.
  
      Args:
          experiment: Experiment.
          data: Data for fitting the model.
  
      Returns: A torch modelbridge with transforms set.
      """
      # Transforms is the minimal set that will work for converting any search
      # space to tensors.
      return TorchModelBridge(
          experiment=experiment,
          search_space=to_nonrobust_search_space(
              experiment.search_space
          ),
          data=data,
          model=TorchModel(),
          transforms=[SearchSpaceToFloat],
          fit_out_of_design=True,
      )
  
imports: import copy
  from copy import deepcopy
  from itertools import combinations
  from logging import Logger
  from typing import cast, Dict, List, NamedTuple, Optional, Tuple, Union
  import numpy
  import torch
  from ax.core.batch_trial import BatchTrial
  from ax.core.data import Data
  from ax.core.experiment import Experiment
  from ax.core.metric import Metric
  from ax.core.objective import ScalarizedObjective
  from ax.core.observation import ObservationFeatures
  from ax.core.optimization_config import MultiObjectiveOptimizationConfig, OptimizationConfig
  from ax.core.outcome_constraint import ComparisonOp, ObjectiveThreshold, OutcomeConstraint
  from ax.core.search_space import RobustSearchSpace, SearchSpace
  from ax.core.types import TParameterization
  from ax.exceptions.core import AxError, UnsupportedError, UserInputError
  from ax.modelbridge.modelbridge_utils import _get_modelbridge_training_data, get_pareto_frontier_and_configs, observed_pareto_frontier
  from ax.modelbridge.registry import Models
  from ax.modelbridge.torch import TorchModelBridge
  from ax.modelbridge.transforms.search_space_to_float import SearchSpaceToFloat
  from ax.models.torch.posterior_mean import get_PosteriorMean
  from ax.models.torch_base import TorchModel
  from ax.utils.common.logger import get_logger
  from ax.utils.stats.statstools import relativize
  from botorch.utils.multi_objective import is_non_dominated
  from botorch.utils.multi_objective.hypervolume import infer_reference_point
label_desc: Use image node_id 6 for calling a global function with example usage: get_tensor_converter_model(experiment, data) and returns: TorchModelBridge