code: def test_merge_importance_dfs_same_model(
      sample_features, sample_importance_df_1, sample_importance_df_2
  ):
      # test the scenario where previous feature importance df exists and its importance estimates come from the same fitted model
      prev_df, curr_df = sample_importance_df_1, sample_importance_df_2
      result_df = merge_importance_dfs(
          prev_df, curr_df, using_prev_fit_fi=set()
      )
      assert [
          score if score == score else None
          for score in result_df["importance"].tolist()
      ] == [0.0, 0.1, 0.1, 1.0, None]
      assert result_df["n"].tolist() == [15, 15, 10, 5, 0]
  
file_path: autogluon/core/tests/unittests/test_feature_selection.py
imports: import numpy
  import pandas
  import pytest
  from numpy.core.fromnumeric import sort
  from autogluon.core.utils.feature_selection import *
  from autogluon.core.utils.utils import unevaluated_fi_df_template
label_desc: Use image node_id 7 for calling a global function with example usage: test_merge_importance_dfs_same_model(sample_features, sample_importance_df_1, sample_importance_df_2) without return types