code: def test_cython_group_mean_not_datetimelike_but_has_NaT_values():
      actual = np.zeros(shape=(1, 1), dtype="float64")
      counts = np.array([0], dtype="int64")
      data = (
          np.array(
              [np.timedelta64("NaT"), np.timedelta64("NaT")],
              dtype="m8[ns]",
          )[:, None]
          .view("int64")
          .astype("float64")
      )
      labels = np.zeros(len(data), dtype=np.intp)
  
      group_mean(actual, counts, data, labels, is_datetimelike=False)
  
      tm.assert_numpy_array_equal(
          actual[:, 0],
          np.array(
              np.divide(np.add(data[0], data[1]), 2), dtype="float64"
          ),
      )
  
imports: import numpy
  import pytest
  from pandas._libs import groupby
  from pandas._libs.groupby import group_cumprod, group_cumsum, group_mean, group_sum, group_var
  from pandas.core.dtypes.common import ensure_platform_int
  from pandas import isna
  import pandas._testing
label_desc: Use image node_id 8 for calling a global function with example usage: test_cython_group_mean_not_datetimelike_but_has_NaT_values() without return types