code: def test_importance_cat_only(data, compute_backend_df):
      num_columns = compute_backend_df.select_dtypes(
          ["number"]
      ).columns.values
      data = data[[c for c in data.columns if c not in num_columns]]
      assert (
          len(dd.importance(data, "d", return_values=True))
          == data.shape[1] - 2
      ), "Wrong size of importance values"
  
file_path: data-describe/tests/test_importance.py
imports: import pytest
  import matplotlib
  import pandas
  import numpy
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.preprocessing import LabelEncoder
  import data_describe
label_desc: Use image node_id 3 for calling a global function with example usage: test_importance_cat_only(data, compute_backend_df) without return types