code: def calculate_single_variable_importance(
      aspect,
      new_observation,
      type,
      N,
      B,
      sample_method,
      f,
      processes,
      random_state,
  ):
      variable_groups = aspect.get_aspects(h=2)
      if type == "default":
          result_df = calculate_predict_aspect_importance(
              aspect.explainer,
              new_observation,
              variable_groups,
              N,
              None,
              sample_method,
              f,
              random_state,
          )
      else:
          result_df = calculate_shap_predict_aspect_importance(
              aspect.explainer,
              new_observation,
              variable_groups,
              N,
              B,
              processes,
              random_state,
          )
      result_df.variable_names = list(
          itertools.chain.from_iterable(result_df.variable_names)
      )
      result_df.variable_values = list(
          itertools.chain.from_iterable(result_df.variable_values)
      )
      result_df = result_df[
          ["variable_names", "variable_values", "importance"]
      ]
      return result_df
  
file_path: DALEX/python/dalex/dalex/aspect/_predict_triplot/utils.py
imports: import numpy
  import pandas
  import itertools
  from copy import deepcopy
  import re
  from dalex.aspect._predict_aspect_importance.utils import calculate_predict_aspect_importance, calculate_shap_predict_aspect_importance
label_desc: Use image node_id 2 for calling a global function with example usage: calculate_single_variable_importance(aspect, new_observation, type, N, B, sample_method, f, processes, random_state) and returns: result_df