code: def test_timegrouper_apply_return_type_value(self):
      # Using `apply` with the `TimeGrouper` should give the
      # same return type as an `apply` with a `Grouper`.
      # Issue #11742
      df = DataFrame(
          {"date": ["10/10/2000", "11/10/2000"], "value": [10, 13]}
      )
      df_dt = df.copy()
      df_dt["date"] = pd.to_datetime(df_dt["date"])
  
      def sumfunc_value(x):
          return x.value.sum()
  
      msg = "DataFrameGroupBy.apply operated on the grouping columns"
      with tm.assert_produces_warning(FutureWarning, match=msg):
          expected = df.groupby(Grouper(key="date")).apply(
              sumfunc_value
          )
      with tm.assert_produces_warning(FutureWarning, match=msg):
          result = df_dt.groupby(Grouper(freq="ME", key="date")).apply(
              sumfunc_value
          )
      tm.assert_series_equal(
          result.reset_index(drop=True), expected.reset_index(drop=True)
      )
  
imports: from datetime import datetime, timedelta
  import numpy
  import pytest
  import pytz
  import pandas
  from pandas import DataFrame, DatetimeIndex, Index, MultiIndex, Series, Timestamp, date_range, offsets
  import pandas._testing
  from pandas.core.groupby.grouper import Grouper
  from pandas.core.groupby.ops import BinGrouper
label_desc: Use image node_id 7 for calling the TestGroupBy obj's underlying member method code with example usage: obj.test_timegrouper_apply_return_type_value() and returns: x