code: def test_rai_dashboard_input_adult_debug_ml_invalid_features(
      self,
      create_rai_insights_object_classification_with_model,
      create_rai_insights_object_classification_with_predictions,
      with_model,
  ):
      if with_model:
          ri = create_rai_insights_object_classification_with_model
      else:
          ri = (
              create_rai_insights_object_classification_with_predictions
          )
      # test invalid features throw errors
      invalid_features = ["Age", "InvalidFeature"]
      filters = []
      composite_filters = []
      max_depth = 3
      num_leaves = 3
      min_child_samples = 8
      metric = "Error rate"
      post_data = [
          invalid_features,
          filters,
          composite_filters,
          max_depth,
          num_leaves,
          min_child_samples,
          metric,
      ]
  
      dashboard_input = ResponsibleAIDashboardInput(ri)
      flask_server_prediction_output = dashboard_input.debug_ml(
          post_data
      )
  
      self.check_failure_criteria(
          flask_server_prediction_output,
          "Failed to generate json tree representation,",
      )
      self.check_failure_criteria(
          flask_server_prediction_output,
          "not found in dataset. Existing features",
      )
  
file_path: responsible-ai-toolbox/raiwidgets/tests/responsibleai_dashboard/test_responsibleai_dashboard_input_erroranalysis.py
imports: import pytest
  from raiwidgets.interfaces import WidgetRequestResponseConstants
  from raiwidgets.responsibleai_dashboard_input import ResponsibleAIDashboardInput
  from .common_utils import CheckResponsibleAIDashboardInputTestResult
label_desc: Use image node_id 6 for calling the TestResponsibleAIDashboardInputClassificationErrorAnalysis obj's underlying member method code with example usage: obj.test_rai_dashboard_input_adult_debug_ml_invalid_features(create_rai_insights_object_classification_with_model, create_rai_insights_object_classification_with_predictions, with_model) without return types