code: def test_date_time(datapath):
      # Support of different SAS date/datetime formats (PR #15871)
      fname = datapath("io", "sas", "data", "datetime.sas7bdat")
      df = pd.read_sas(fname)
      fname = datapath("io", "sas", "data", "datetime.csv")
      df0 = pd.read_csv(
          fname,
          parse_dates=[
              "Date1",
              "Date2",
              "DateTime",
              "DateTimeHi",
              "Taiw",
          ],
      )
      # GH 19732: Timestamps imported from sas will incur floating point errors
      # See GH#56014 for discussion of the correct "expected" results
      #  We are really just testing that we are "close". This only seems to be
      #  an issue near the implementation bounds.
  
      df[df.columns[3]] = df.iloc[:, 3].dt.round("us")
      df0["Date1"] = df0["Date1"].astype("M8[s]")
      df0["Date2"] = df0["Date2"].astype("M8[s]")
      df0["DateTime"] = df0["DateTime"].astype("M8[ms]")
      df0["Taiw"] = df0["Taiw"].astype("M8[s]")
  
      res = df0["DateTimeHi"].astype("M8[us]").dt.round("ms")
      df0["DateTimeHi"] = res.astype("M8[ms]")
  
      if not IS64:
          # No good reason for this, just what we get on the CI
          df0.loc[0, "DateTimeHi"] += np.timedelta64(1, "ms")
          df0.loc[[2, 3], "DateTimeHi"] -= np.timedelta64(1, "ms")
      tm.assert_frame_equal(df, df0)
  
imports: import contextlib
  from datetime import datetime
  import io
  import os
  from pathlib import Path
  import numpy
  import pytest
  from pandas.compat import IS64
  from pandas.errors import EmptyDataError
  import pandas.util._test_decorators
  import pandas
  import pandas._testing
  from pandas.io.sas.sas7bdat import SAS7BDATReader
label_desc: Use image node_id 8 for calling a global function with example usage: test_date_time(datapath) without return types