code: def test_fairness_interpretability_dashboard_loan_allocation():
      nb_path = INDIVIDUAL_DASHBOARDS
      nb_name = "fairness-interpretability-dashboard-income-prediction"
  
      test_values = {}
  
      test_values["race_group_sizes"] = ScrapSpec(
          "X_raw['race'].value_counts().to_dict()",
          RACE_GROUP_SIZES_ADULT,
      )
  
      test_values["local_explanation_feature_names"] = ScrapSpec(
          "sorted_local_importance_names[0][0:3]",
          ["marital-status", "occupation", "hours-per-week"],
      )
  
      test_values["dominant_models"] = ScrapSpec(
          "list(dominant_all.keys())",
          [
              "unmitigated",
              "grid_29",
              "grid_30",
              "grid_31",
              "grid_32",
              "grid_33",
              "grid_34",
              "grid_35",
          ],
      )
  
      assay_one_notebook(nb_path, nb_name, test_values)
  
imports: import os
  import nbformat
  import papermill
  import pytest
  import scrapbook
label_desc: Use image node_id 4 for calling a global function with example usage: test_fairness_interpretability_dashboard_loan_allocation() without return types