code: def _test_downsample_windowed_classifier(self):
      class IntSumClassifier(CustomClassifier):
          inputs = [TestingDataSet.float_col]
          window_length = 8
          dtype = int64_dtype
          missing_value = -1
  
          def compute(self, today, assets, out, floats):
              out[:] = floats.sum(axis=0).astype(int) % 4
  
      self.check_downsampled_term(IntSumClassifier())
  
imports: import pandas
  from pandas.util.testing import assert_frame_equal
  from catalyst.pipeline import Pipeline, CustomFactor, CustomFilter, CustomClassifier
  from catalyst.pipeline.data.testing import TestingDataSet
  from catalyst.pipeline.factors.equity import SimpleMovingAverage
  from catalyst.pipeline.filters.smoothing import All
  from catalyst.testing import CatalystTestCase, parameter_space
  from catalyst.testing.fixtures import WithTradingSessions, WithSeededRandomPipelineEngine
  from catalyst.utils.input_validation import _qualified_name
  from catalyst.utils.numpy_utils import int64_dtype
label_desc: Use image node_id 6 for calling the DownsampledPipelineTestCase obj's underlying member method code with example usage: obj._test_downsample_windowed_classifier() without return types