code: def test_append_to_multiple_dropna(setup_path):
      df1 = DataFrame(
          np.random.default_rng(2).standard_normal((10, 4)),
          columns=Index(list("ABCD"), dtype=object),
          index=date_range("2000-01-01", periods=10, freq="B"),
      )
      df2 = DataFrame(
          np.random.default_rng(2).standard_normal((10, 4)),
          columns=Index(list("ABCD"), dtype=object),
          index=date_range("2000-01-01", periods=10, freq="B"),
      ).rename(columns="{}_2".format)
      df1.iloc[1, df1.columns.get_indexer(["A", "B"])] = np.nan
      df = concat([df1, df2], axis=1)
  
      with ensure_clean_store(setup_path) as store:
          # dropna=True should guarantee rows are synchronized
          store.append_to_multiple(
              {"df1": ["A", "B"], "df2": None},
              df,
              selector="df1",
              dropna=True,
          )
          result = store.select_as_multiple(["df1", "df2"])
          expected = df.dropna()
          tm.assert_frame_equal(result, expected, check_index_type=True)
          tm.assert_index_equal(
              store.select("df1").index, store.select("df2").index
          )
  
imports: import datetime
  from datetime import timedelta
  import re
  import numpy
  import pytest
  from pandas._libs.tslibs import Timestamp
  import pandas
  from pandas import DataFrame, Index, Series, _testing, concat, date_range, read_hdf
  from pandas.tests.io.pytables.common import _maybe_remove, ensure_clean_store
label_desc: Use image node_id 17 for calling a global function with example usage: test_append_to_multiple_dropna(setup_path) without return types