code: def ax_parameter_sens(
      model_bridge: TorchModelBridge,
      metrics: Optional[List[str]] = None,
      order: str = "first",
      **sobol_kwargs: Any,
  ) -> Dict[str, Dict[str, np.ndarray]]:
      """
      Compute sensitivity for all metrics on an TorchModelBridge.
  
      Args:
          model_bridge: A ModelBridge object with models that were fit.
          metrics: The names of the metrics and outcomes for which to compute
              sensitivities. This should preferably be metrics with a good model fit.
              Defaults to model_bridge.outcomes.
          order: A string specifying the order of the Sobol indices to be computed.
              Supports "first" and "total" and defaults to "first".
          sobol_kwargs: keyword arguments passed on to SobolSensitivityGPMean.
  
      Returns:
          Dictionary {'metric_name': {'parameter_name': sensitivity_value}}, where the
              `sensitivity` value is cast to a Numpy array in order to be compatible with
              `plot_feature_importance_by_feature`.
      """
      if metrics is None:
          metrics = model_bridge.outcomes
      # can safely access _search_space_digest after type check
      torch_model = _get_torch_model(model_bridge)
      digest = torch_model.search_space_digest
      ind = compute_sobol_indices_from_model_list(
          model_list=_get_model_per_metric(torch_model, metrics),
          bounds=torch.tensor(
              digest.bounds
          ).T,  # transposing to make it 2 x d
          order=order,
          **sobol_kwargs,
      )
      return _array_with_string_indices_to_dict(
          rows=metrics, cols=digest.feature_names, A=ind.numpy()
      )
  
imports: from copy import deepcopy
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import torch
  from ax.modelbridge.torch import TorchModelBridge
  from ax.models.torch.botorch import BotorchModel
  from ax.models.torch.botorch_modular.model import BoTorchModel
  from ax.utils.common.typeutils import checked_cast
  from botorch.models.model import Model, ModelList
  from botorch.posteriors.gpytorch import GPyTorchPosterior
  from botorch.sampling.normal import SobolQMCNormalSampler
  from botorch.utils.sampling import draw_sobol_samples
  from botorch.utils.transforms import unnormalize
  from torch import Tensor
label_desc: Use image node_id 4 for calling a global function with example usage: ax_parameter_sens(model_bridge, metrics, order) and returns: _array_with_string_indices_to_dict