code: def partial_dependencies(
      experiment,
      with_evc_tree=True,
      params=None,
      smoothing=0.85,
      verbose_hover=True,
      n_grid_points=10,
      n_samples=50,
      colorscale="Blues",
      model="RandomForestRegressor",
      model_kwargs=None,
  ):
      """
      Make contour plots to visualize the search space of each combination of params.
  
      Parameters
      ----------
      experiment: ExperimentClient or Experiment
          The orion object containing the experiment data
  
      with_evc_tree: bool, optional
          Fetch all trials from the EVC tree.
          Default: True
  
      params: list of str, optional
          Indicates the parameters to include in the plots. All parameters are included by default.
  
      smoothing: float, optional
          Smoothing applied to the countor plot. 0 corresponds to no smoothing. Default is 0.85.
  
      verbose_hover: bool
          Indicates whether to display the hyperparameter in hover tooltips. True by default.
  
      colorscale: str, optional
          The colorscale used for the contour plots. Supported values depends on the backend.
          Default is 'Blues'.
  
      n_grid_points: int, optional
          Number of points in the grid to compute partial dependency. Default is 10.
  
      n_samples: int, optional
          Number of samples to randomly generate the grid used to compute the partial dependency.
          Default is 50.
  
      model: str
          Name of the regression model to use. Can be one of
          - AdaBoostRegressor
          - BaggingRegressor
          - ExtraTreesRegressor
          - GradientBoostingRegressor
          - RandomForestRegressor (Default)
  
      model_kwargs: dict, optional
          Arguments for the regressor model.
  
      Returns
      -------
      plotly.graph_objects.Figure
  
      Raises
      ------
      ValueError
          If no experiment is provided.
  
      """
  
      return backend.partial_dependencies(
          experiment,
          with_evc_tree=with_evc_tree,
          params=params,
          smoothing=smoothing,
          verbose_hover=verbose_hover,
          n_grid_points=n_grid_points,
          n_samples=n_samples,
          colorscale=colorscale,
          model=model,
          model_kwargs=model_kwargs,
      )
  
file_path: orion/src/orion/plotting/base.py
imports: import orion.plotting.backend_plotly
label_desc: Use image node_id 3 for calling a global function with example usage: partial_dependencies(experiment, with_evc_tree, params, smoothing, verbose_hover, n_grid_points, n_samples, colorscale, model, model_kwargs) and returns: backend