code: def load_intermediate_data():
      df = pandas.DataFrame(
          [
              {
                  "censored": True,
                  "conc": 5.0,
                  "det_limit_index": 1,
                  "rank": 1,
              },
              {
                  "censored": True,
                  "conc": 5.0,
                  "det_limit_index": 1,
                  "rank": 2,
              },
              {
                  "censored": True,
                  "conc": 5.5,
                  "det_limit_index": 2,
                  "rank": 1,
              },
              {
                  "censored": True,
                  "conc": 5.75,
                  "det_limit_index": 3,
                  "rank": 1,
              },
              {
                  "censored": True,
                  "conc": 9.5,
                  "det_limit_index": 4,
                  "rank": 1,
              },
              {
                  "censored": True,
                  "conc": 9.5,
                  "det_limit_index": 4,
                  "rank": 2,
              },
              {
                  "censored": True,
                  "conc": 11.0,
                  "det_limit_index": 5,
                  "rank": 1,
              },
              {
                  "censored": False,
                  "conc": 2.0,
                  "det_limit_index": 0,
                  "rank": 1,
              },
              {
                  "censored": False,
                  "conc": 4.2,
                  "det_limit_index": 0,
                  "rank": 2,
              },
              {
                  "censored": False,
                  "conc": 4.62,
                  "det_limit_index": 0,
                  "rank": 3,
              },
              {
                  "censored": False,
                  "conc": 5.57,
                  "det_limit_index": 2,
                  "rank": 1,
              },
              {
                  "censored": False,
                  "conc": 5.66,
                  "det_limit_index": 2,
                  "rank": 2,
              },
              {
                  "censored": False,
                  "conc": 5.86,
                  "det_limit_index": 3,
                  "rank": 1,
              },
              {
                  "censored": False,
                  "conc": 6.65,
                  "det_limit_index": 3,
                  "rank": 2,
              },
              {
                  "censored": False,
                  "conc": 6.78,
                  "det_limit_index": 3,
                  "rank": 3,
              },
              {
                  "censored": False,
                  "conc": 6.79,
                  "det_limit_index": 3,
                  "rank": 4,
              },
              {
                  "censored": False,
                  "conc": 7.5,
                  "det_limit_index": 3,
                  "rank": 5,
              },
              {
                  "censored": False,
                  "conc": 7.5,
                  "det_limit_index": 3,
                  "rank": 6,
              },
              {
                  "censored": False,
                  "conc": 7.5,
                  "det_limit_index": 3,
                  "rank": 7,
              },
              {
                  "censored": False,
                  "conc": 8.63,
                  "det_limit_index": 3,
                  "rank": 8,
              },
              {
                  "censored": False,
                  "conc": 8.71,
                  "det_limit_index": 3,
                  "rank": 9,
              },
              {
                  "censored": False,
                  "conc": 8.99,
                  "det_limit_index": 3,
                  "rank": 10,
              },
              {
                  "censored": False,
                  "conc": 9.85,
                  "det_limit_index": 4,
                  "rank": 1,
              },
              {
                  "censored": False,
                  "conc": 10.82,
                  "det_limit_index": 4,
                  "rank": 2,
              },
              {
                  "censored": False,
                  "conc": 11.25,
                  "det_limit_index": 5,
                  "rank": 1,
              },
              {
                  "censored": False,
                  "conc": 11.25,
                  "det_limit_index": 5,
                  "rank": 2,
              },
              {
                  "censored": False,
                  "conc": 12.2,
                  "det_limit_index": 5,
                  "rank": 3,
              },
              {
                  "censored": False,
                  "conc": 14.92,
                  "det_limit_index": 5,
                  "rank": 4,
              },
              {
                  "censored": False,
                  "conc": 16.77,
                  "det_limit_index": 5,
                  "rank": 5,
              },
              {
                  "censored": False,
                  "conc": 17.81,
                  "det_limit_index": 5,
                  "rank": 6,
              },
              {
                  "censored": False,
                  "conc": 19.16,
                  "det_limit_index": 5,
                  "rank": 7,
              },
              {
                  "censored": False,
                  "conc": 19.19,
                  "det_limit_index": 5,
                  "rank": 8,
              },
              {
                  "censored": False,
                  "conc": 19.64,
                  "det_limit_index": 5,
                  "rank": 9,
              },
              {
                  "censored": False,
                  "conc": 20.18,
                  "det_limit_index": 5,
                  "rank": 10,
              },
              {
                  "censored": False,
                  "conc": 22.97,
                  "det_limit_index": 5,
                  "rank": 11,
              },
          ]
      )
  
      return df
  
imports: from statsmodels.compat.pandas import assert_series_equal, assert_frame_equal
  from io import StringIO
  from textwrap import dedent
  import numpy
  import numpy.testing
  import numpy
  from numpy.testing import assert_equal
  import pandas
  import pytest
  from statsmodels.imputation import ros
label_desc: Use image node_id 2 for calling a global function with example usage: load_intermediate_data() and returns: df