code: def from_dict(cls, obj_dict):
          
  
          # convert the output into a proper form
          obj_dict['output'] = helper.rec_dict_to_numpy_dict(obj_dict["output"])
  
          helper.dict_to_numpy_dict(obj_dict['output'])
          if "trained_global_model" in obj_dict.keys():
              raise Exception("Found trained_global_model feature in dictionary. Use ConciseCV.load to load this file.")
          dc = Concise(**obj_dict["param"])
  
          # touch the hidden arguments
          dc._param = obj_dict["param"]
          if obj_dict["output"]["weights"] is None:
              dc._model_fitted = False
          else:
              dc._model_fitted = True
              dc._exec_time = obj_dict["execution_time"]
  
          dc.unused_param = obj_dict["unused_param"]
          dc._accuracy = obj_dict["output"]["accuracy"]
          dc._splines = obj_dict["output"]["splines"]
  
          weights = obj_dict["output"]["weights"]
  
          if weights is not None:
              # fix the dimensionality of X_feat in case it was 0 dimensional
              if weights["feature_weights"].shape == (0,):
                  weights["feature_weights"].shape = (0, obj_dict["param"]["num_tasks"])
              dc._set_var_res(weights)
  
          return dc
docs: Load the object from a dictionary (produced with :py:func:`Concise.to_dict`)
  
          Returns:
              Concise: Loaded Concise object.