code: def test_format_summary_df():
      # tests short column names
      summary_df = pd.DataFrame(
          {
              "Pred_col": ["Intercept", "x"],
              "Estimate": [123456, 0.00545],
              "Std. Err": [12346, 0.00072],
              "t value": [14.588889, 3.6799934],
              "z value": [14.588889, 3.6799934],
              "Pr(>|t|)": [1e-18, 0.00066],
              "Pr(>|Z|)": [1e-18, 0.00066],
              "Pr(>)_boot": [1e-18, 0.00066],
              "Pr(>)_split": [1e-18, 0.00066],
              "sig. code": ["***", "***"],
              "95%CI": [[120000, 130000], [0.005, 0.006]],
              "Feature importance": [123456, 0.00545],
          }
      )
      formatted_summary_df = format_summary_df(summary_df)
      assert formatted_summary_df.equals(
          pd.DataFrame(
              {
                  "Pred_col": ["Intercept", "x"],
                  "Estimate": ["1.235e+05", "0.00545"],
                  "Std. Err": ["1.235e+04", "0.00072"],
                  "t value": ["14.59", "3.68"],
                  "z value": ["14.59", "3.68"],
                  "Pr(>|t|)": ["<2e-16", "6.60e-04"],
                  "Pr(>|Z|)": ["<2e-16", "6.60e-04"],
                  "Pr(>)_boot": ["<2e-16", "6.60e-04"],
                  "Pr(>)_split": ["<2e-16", "6.60e-04"],
                  "sig. code": ["***", "***"],
                  "95%CI": [
                      ("1.200e+05", "1.300e+05"),
                      ("0.005", "0.006"),
                  ],
                  "Feature importance": ["1.235e+05", "0.00545"],
              }
          )
      )
      # tests long column names
      summary_df = pd.DataFrame(
          {
              "Pred_col": [
                  "Intercept",
                  "i_have_longer_than_20_characters",
              ],
              "Estimate": [123456, 0.00545],
              "Std. Err": [12346, 0.00072],
              "t value": [14.588889, 3.6799934],
              "Pr(>|t|)": [1e-18, 0.00066],
              "sig. code": ["***", "***"],
              "95%CI": [[120000, 130000], [0.005, 0.006]],
          }
      )
      formatted_summary_df = format_summary_df(summary_df)
      assert formatted_summary_df.equals(
          pd.DataFrame(
              {
                  "Pred_col": ["Intercept", "i_have_l...aracters"],
                  "Estimate": ["1.235e+05", "0.00545"],
                  "Std. Err": ["1.235e+04", "0.00072"],
                  "t value": ["14.59", "3.68"],
                  "Pr(>|t|)": ["<2e-16", "6.60e-04"],
                  "sig. code": ["***", "***"],
                  "95%CI": [
                      ("1.200e+05", "1.300e+05"),
                      ("0.005", "0.006"),
                  ],
              }
          )
      )
  
file_path: greykite/greykite/tests/algo/common/test_model_summary_utils.py
imports: import numpy
  import pandas
  import pytest
  from sklearn.linear_model import RidgeCV
  from greykite.algo.common.model_summary_utils import Bootstrapper
  from greykite.algo.common.model_summary_utils import format_summary_df
  from greykite.algo.common.model_summary_utils import get_info_dict_lm
  from greykite.algo.common.model_summary_utils import print_summary
  from greykite.algo.common.model_summary_utils import process_intercept
  from greykite.algo.common.model_summary_utils import round_numbers
label_desc: Use image node_id 3 for calling a global function with example usage: test_format_summary_df() without return types