code: def get_sensitivity_values(ax_model: ModelBridge) -> Dict:
      """
      Compute lengscale sensitivity value for on an ax model.
  
      Returns map {'metric_name': {'parameter_name': sensitivity_value}}
      """
      ls = (
          ax_model.model.model.covar_module.base_kernel.lengthscale.squeeze()
      )
      if len(ls.shape) > 1:
          ls = ls.mean(dim=0)
      # pyre-fixme[16]: `float` has no attribute `detach`.
      importances_tensor = torch.stack([(1 / ls).detach().cpu()])
      importances_dict = dict(
          zip(ax_model.outcomes, importances_tensor)
      )
      res = {}
      for metric_name in ax_model.outcomes:
          importances_arr = importances_dict[metric_name].numpy()
          # pyre-fixme[16]: `ModelBridge` has no attribute `parameters`.
          res[metric_name] = dict(
              zip(ax_model.parameters, importances_arr)
          )
      return res
  
file_path: Ax/ax/plot/tests/test_feature_importances.py
imports: from typing import Dict
  import torch
  from ax.modelbridge.base import ModelBridge
  from ax.modelbridge.registry import Models
  from ax.plot.base import AxPlotConfig
  from ax.plot.feature_importances import plot_feature_importance_by_feature, plot_feature_importance_by_feature_plotly, plot_feature_importance_by_metric, plot_feature_importance_by_metric_plotly, plot_relative_feature_importance, plot_relative_feature_importance_plotly
  from ax.utils.common.testutils import TestCase
  from ax.utils.testing.core_stubs import get_branin_experiment
  from ax.utils.testing.mock import fast_botorch_optimize
  from plotly import graph_objects
label_desc: Use image node_id 2 for calling a global function with example usage: get_sensitivity_values(ax_model) and returns: res