code: def test_warning_on_non_uniform_time_steps(self):
      with warnings.catch_warnings(record=True) as w:
          first_class = pd.DataFrame(
              {
                  "a": [1, 2, 3, 4],
                  "b": [5, 6, 7, 8],
                  "time": [1, 2, 4, 5],
              }
          )
          second_class = pd.DataFrame(
              {
                  "a": [10, 11],
                  "b": [12, 13],
                  "time": list(range(20, 22)),
              }
          )
  
          first_class["id"] = 1
          second_class["id"] = 2
  
          df_full = pd.concat(
              [first_class, second_class], ignore_index=True
          )
  
          dataframe_functions.roll_time_series(
              df_full,
              column_id="id",
              column_sort="time",
              column_kind=None,
              rolling_direction=1,
              n_jobs=0,
          )
  
          self.assertGreaterEqual(len(w), 1)
          self.assertIn(
              "Your time stamps are not uniformly sampled, which makes rolling "
              "nonsensical in some domains.",
              [str(warning.message) for warning in w],
          )
  
imports: import warnings
  from unittest import TestCase
  import numpy
  import pandas
  from pandas.testing import assert_frame_equal, assert_series_equal
  from tests.fixtures import warning_free
  from tsfresh import extract_relevant_features
  from tsfresh.feature_extraction.settings import MinimalFCParameters
  from tsfresh.utilities import dataframe_functions
label_desc: Use image node_id 10 for calling the RollingTestCase obj's underlying member method code with example usage: obj.test_warning_on_non_uniform_time_steps() without return types