code: def importances(self):
      try:
          scores = self._error_analyzer.compute_importances()
          return {WidgetRequestResponseConstants.DATA: scores}
      except Exception as e:
          print(e)
          traceback.print_exc()
          return {
              WidgetRequestResponseConstants.ERROR: "Failed to generate feature importances",
              WidgetRequestResponseConstants.DATA: [],
          }
  
imports: import traceback
  import numpy
  import pandas
  from erroranalysis._internal.constants import Metrics, display_name_to_metric, metric_to_display_name
  from erroranalysis._internal.error_analyzer import ModelAnalyzer, PredictionsAnalyzer
  from erroranalysis._internal.utils import is_spark
  from raiutils.data_processing import convert_to_list, serialize_json_safe
  from raiutils.models import is_classifier
  from responsibleai._interfaces import ErrorAnalysisData
  from .constants import ModelTask
  from .error_analysis_constants import ErrorAnalysisDashboardInterface, MethodConstants
  from .error_handling import _format_exception
  from .explanation_constants import ExplanationDashboardInterface, WidgetRequestResponseConstants
label_desc: Use image node_id 12 for calling the ErrorAnalysisDashboardInput obj's underlying member method code with example usage: obj.importances() and returns: dict, dict