code: def test_importance_num_only(data, compute_backend_df):
      data = compute_backend_df.select_dtypes(["number"])
      rfr = RandomForestRegressor(random_state=1)
      assert isinstance(
          dd.importance(data, "a", estimator=rfr, return_values=True),
          np.ndarray,
      ), "Importance values not a numpy array"
  
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 2 for calling a global function with example usage: test_importance_num_only(data, compute_backend_df) without return types