docstr: Test that masking a factor that uses slices as inputs does not mask the
  slice data.
docstr_tok: ["Test","that","masking","a","factor","that","uses","slices","as","inputs","does","not","mask","the","slice","data","."]
code: def _test_slice_with_masking(self, unmasked_column, slice_column):
      """
      Test that masking a factor that uses slices as inputs does not mask the
      slice data.
      """
      sids = self.sids
      asset_finder = self.asset_finder
      start_date = self.pipeline_start_date
      end_date = self.pipeline_end_date
  
      # Create a filter that masks out all but a single asset.
      unmasked_asset = asset_finder.retrieve_asset(
          sids[unmasked_column]
      )
      unmasked_asset_only = AssetID().eq(unmasked_asset.sid)
  
      # Asset used to create our slice. In the cases where this is different
      # than `unmasked_asset`, our slice should still have non-missing data
      # when used as an input to our custom factor. That is, it should not be
      # masked out.
      slice_asset = asset_finder.retrieve_asset(sids[slice_column])
  
      returns = Returns(window_length=2, inputs=[self.col])
      returns_slice = returns[slice_asset]
  
      returns_results = self.run_pipeline(
          Pipeline(columns={"returns": returns}),
          start_date,
          end_date,
      )
      returns_results = returns_results["returns"].unstack()
  
      class UsesSlicedInput(CustomFactor):
          window_length = 1
          inputs = [returns, returns_slice]
  
          def compute(self, today, assets, out, returns, returns_slice):
              # Ensure that our mask correctly affects the `returns` input
              # and does not affect the `returns_slice` input.
              assert returns.shape == (1, 1)
              assert returns_slice.shape == (1, 1)
              assert (
                  returns[0, 0]
                  == returns_results.loc[today, unmasked_asset]
              )
              assert (
                  returns_slice[0, 0]
                  == returns_results.loc[today, slice_asset]
              )
  
      columns = {"masked": UsesSlicedInput(mask=unmasked_asset_only)}
  
      # Assertions about the expected data are made in the `compute` function
      # of our custom factor above.
      self.run_pipeline(Pipeline(columns=columns), start_date, end_date)
  
imports: from numpy import where
  from pandas import Int64Index, Timestamp
  from pandas.util.testing import assert_frame_equal
  from catalyst.assets import Asset
  from catalyst.errors import NonExistentAssetInTimeFrame, NonSliceableTerm, NonWindowSafeInput, UnsupportedPipelineOutput
  from catalyst.pipeline import CustomFactor, Pipeline
  from catalyst.pipeline.data import USEquityPricing
  from catalyst.pipeline.data.testing import TestingDataSet
  from catalyst.pipeline.factors.equity import Returns, RollingLinearRegressionOfReturns, RollingPearsonOfReturns, RollingSpearmanOfReturns, SimpleMovingAverage
  from catalyst.testing import AssetID, AssetIDPlusDay, check_arrays, OpenPrice, parameter_space
  from catalyst.testing.fixtures import WithSeededRandomPipelineEngine, CatalystTestCase
  from catalyst.utils.numpy_utils import datetime64ns_dtype
label_desc: Use image node_id 3 for calling the SliceTestCase obj's underlying member method code with example usage: obj._test_slice_with_masking(unmasked_column, slice_column) without return types