code: def test_groupby_dropna_datetime_like_data(
      dropna,
      values,
      datetime1,
      datetime2,
      unique_nulls_fixture,
      unique_nulls_fixture2,
  ):
      # 3729
      df = pd.DataFrame(
          {
              "values": [1, 2, 3, 4, 5, 6],
              "dt": [
                  datetime1,
                  unique_nulls_fixture,
                  datetime2,
                  unique_nulls_fixture2,
                  datetime1,
                  datetime1,
              ],
          }
      )
  
      if dropna:
          indexes = [datetime1, datetime2]
      else:
          indexes = [datetime1, datetime2, np.nan]
  
      grouped = df.groupby("dt", dropna=dropna).agg({"values": "sum"})
      expected = pd.DataFrame(
          {"values": values}, index=pd.Index(indexes, name="dt")
      )
  
      tm.assert_frame_equal(grouped, expected)
  
imports: import numpy
  import pytest
  from pandas.compat.pyarrow import pa_version_under10p1
  from pandas.core.dtypes.missing import na_value_for_dtype
  import pandas
  import pandas._testing
  from pandas.tests.groupby import get_groupby_method_args
label_desc: Use image node_id 9 for calling a global function with example usage: test_groupby_dropna_datetime_like_data(dropna, values, datetime1, datetime2, unique_nulls_fixture, unique_nulls_fixture2) without return types