code: def calculate_single_variable_importance(
      aspect, loss_function, type, N, B, processes, random_state
  ):
      variable_groups = aspect.get_aspects(h=2)
      mai_object = ModelAspectImportance(
          variable_groups,
          loss_function,
          type,
          N,
          B,
          processes=processes,
          random_state=random_state,
      )
      mai_object.fit(aspect.explainer)
  
      full_result_df = mai_object.result
      single_vi = full_result_df[
          (full_result_df.aspect_name != "_full_model_")
          & (full_result_df.aspect_name != "_baseline_")
      ]
      single_vi.variable_names = list(
          itertools.chain.from_iterable(single_vi.variable_names)
      )
      single_vi = single_vi[
          ["variable_names", "dropout_loss", "dropout_loss_change"]
      ]
      full_result_df["h"] = 1
  
      return single_vi, full_result_df
  
imports: import pandas
  import itertools
  from copy import deepcopy
  from dalex.aspect._model_aspect_importance.object import ModelAspectImportance
label_desc: Use image node_id 2 for calling a global function with example usage: calculate_single_variable_importance(aspect, loss_function, type, N, B, processes, random_state) and returns: single_vi,  full_result_df