code: def test_nat_values():
      dataframe = pd.DataFrame(
          [
              {
                  "ORDER_DATE": date(2011, 3, 1),
                  "SALES": 457.568,
                  "NULL_TEST": Timestamp(
                      "2011-03-01 00:00:00+0000", tz="UTC"
                  ),
              },
              {
                  "ORDER_DATE": date(2011, 3, 1),
                  "SALES": 376.509,
                  "NULL_TEST": Timestamp(
                      "2011-03-01 00:00:00+0000", tz="UTC"
                  ),
              },
              {
                  "ORDER_DATE": date(2011, 3, 1),
                  "SALES": 362.25,
                  "NULL_TEST": Timestamp(
                      "2011-03-01 00:00:00+0000", tz="UTC"
                  ),
              },
              {
                  "ORDER_DATE": date(2011, 3, 1),
                  "SALES": 129.552,
                  "NULL_TEST": Timestamp(
                      "2011-03-01 00:00:00+0000", tz="UTC"
                  ),
              },
              {
                  "ORDER_DATE": date(2011, 3, 1),
                  "SALES": 18.84,
                  "NULL_TEST": NaT,
              },
              {
                  "ORDER_DATE": date(2011, 4, 1),
                  "SALES": 66.96,
                  "NULL_TEST": Timestamp(
                      "2011-04-01 00:00:00+0000", tz="UTC"
                  ),
              },
              {
                  "ORDER_DATE": date(2011, 4, 1),
                  "SALES": 6.24,
                  "NULL_TEST": NaT,
              },
              {
                  "ORDER_DATE": date(2011, 6, 1),
                  "SALES": 881.93,
                  "NULL_TEST": Timestamp(
                      "2011-06-01 00:00:00+0000", tz="UTC"
                  ),
              },
              {
                  "ORDER_DATE": date(2011, 6, 1),
                  "SALES": 166.72,
                  "NULL_TEST": Timestamp(
                      "2011-06-01 00:00:00+0000", tz="UTC"
                  ),
              },
              {
                  "ORDER_DATE": date(2011, 6, 1),
                  "SALES": 25.92,
                  "NULL_TEST": NaT,
              },
          ]
      )
  
      spec = nat_bar_spec()
  
      datasets, warnings = vf.runtime.pre_transform_datasets(
          spec, ["dataframe"], inline_datasets=dict(dataframe=dataframe)
      )
      assert len(warnings) == 0
      assert len(datasets) == 1
  
      dataset = datasets[0]
      assert dataset.to_dict("records")[0] == {
          "NULL_TEST": pd.Timestamp(
              "2011-03-01 00:00:00+0000", tz="UTC"
          ),
          "ORDER_DATE": Timestamp("2011-03-01 00:00:00+0000", tz="UTC"),
          "SALES": 457.568,
          "SALES_end": 457.568,
          "SALES_start": 0.0,
      }
  
imports: import pandas
  import pytest
  from pandas import Timestamp, NaT
  import pyarrow
  import vegafusion
  import json
  import polars
  from datetime import date
  import decimal
  import base64
label_desc: Use image node_id 21 for calling a global function with example usage: test_nat_values() without return types