code: def get_transform_helper_model(
      experiment: Experiment,
      data: Data,
      transforms: Optional[List[Type[Transform]]] = None,
  ) -> MapTorchModelBridge:
      """
      Constructs a TorchModelBridge, to be used as a helper for transforming parameters.
      We perform the default `Cont_X_trans` for parameters but do not perform any
      transforms on the observations.
  
      Args:
          experiment: Experiment.
          data: Data for fitting the model.
  
      Returns: A torch modelbridge.
      """
      if transforms is None:
          transforms = Cont_X_trans + [MapUnitX] + Y_trans
      return MapTorchModelBridge(
          experiment=experiment,
          search_space=experiment.search_space,
          data=data,
          model=TorchModel(),
          transforms=transforms,
          fit_out_of_design=True,
      )
  
imports: import logging
  from abc import ABC, abstractmethod
  from dataclasses import dataclass
  from logging import Logger
  from typing import Any, Dict, Iterable, List, Optional, Sequence, Set, Tuple, Type
  import numpy
  import pandas
  from ax.core.base_trial import TrialStatus
  from ax.core.data import Data
  from ax.core.experiment import Experiment
  from ax.core.map_data import MapData
  from ax.core.objective import MultiObjective
  from ax.modelbridge.map_torch import MapTorchModelBridge
  from ax.modelbridge.modelbridge_utils import _unpack_observations, observation_data_to_array, observation_features_to_array
  from ax.modelbridge.registry import Cont_X_trans, Y_trans
  from ax.modelbridge.transforms.base import Transform
  from ax.modelbridge.transforms.map_unit_x import MapUnitX
  from ax.models.torch_base import TorchModel
  from ax.utils.common.base import Base
  from ax.utils.common.logger import get_logger
  from ax.utils.common.typeutils import checked_cast, not_none
label_desc: Use image node_id 1 for calling a global function with example usage: get_transform_helper_model(experiment, data, transforms) and returns: MapTorchModelBridge