code: def testGaussianizeRagged(self, input_dtype):
      tft_unit.skip_if_not_tf2(
          "RaggedFeature is not available in TF 1.x."
      )
  
      def preprocessing_fn(inputs):
          x_gaussianized = tft.scale_to_gaussian(
              tf.cast(inputs["x"], input_dtype)
          )
          self.assertEqual(
              x_gaussianized.dtype,
              impl_test._mean_output_dtype(input_dtype),
          )
          return {"x_gaussianized": tf.cast(x_gaussianized, tf.float32)}
  
      input_data_values = [
          516,
          -871,
          737,
          415,
          584,
          583,
          152,
          479,
          576,
          409,
          591,
          844,
          -16,
          508,
          669,
          617,
          502,
          532,
          517,
          479,
      ]
      input_data = []
      for idx, v in enumerate(input_data_values):
          input_data.append(
              {
                  "val": [v, -input_data_values[-1 - idx]],
                  "row_lengths_1": [2, 1, 0],
                  "row_lengths_2": [1, 0, 1],
              }
          )
      input_metadata = tft.DatasetMetadata.from_feature_spec(
          {
              "x": tf.io.RaggedFeature(
                  tft_unit.canonical_numeric_dtype(input_dtype),
                  value_key="val",
                  partitions=[
                      tf.io.RaggedFeature.RowLengths(
                          "row_lengths_1"
                      ),  # pytype: disable=attribute-error
                      tf.io.RaggedFeature.RowLengths(
                          "row_lengths_2"
                      ),  # pytype: disable=attribute-error
                  ],
              ),
          }
      )
      expected_data_values = [
          0.91555131,
          -1.54543642,
          1.30767697,
          0.73634456,
          1.03620536,
          1.03443104,
          0.26969729,
          0.84990131,
          1.02201077,
          0.72569862,
          1.04862563,
          1.49752966,
          -0.02838919,
          0.90135672,
          1.18702292,
          1.09475806,
          0.89071077,
          0.9439405,
          0.91732564,
          0.84990131,
      ]
      expected_data = []
      for idx, v in enumerate(expected_data_values):
          expected_data.append(
              {
                  "x_gaussianized$ragged_values": (
                      [v, -expected_data_values[-1 - idx]]
                  ),
                  "x_gaussianized$row_lengths_1": [2, 1, 0],
                  "x_gaussianized$row_lengths_2": [1, 0, 1],
              }
          )
  
      self.assertAnalyzeAndTransformResults(
          input_data,
          input_metadata,
          preprocessing_fn,
          expected_data,
          desired_batch_size=20,
          # Runs the test deterministically on the whole batch.
          beam_pipeline=beam.Pipeline(),
      )
  
file_path: transform/tensorflow_transform/beam/tukey_hh_params_integration_test.py
imports: import itertools
  import apache_beam
  import numpy
  import tensorflow
  import tensorflow_transform
  from tensorflow_transform.beam import impl
  from tensorflow_transform.beam import impl_test
  from tensorflow_transform.beam import tft_unit
label_desc: Use image node_id 5 for calling the TukeyHHParamsIntegrationTest obj's underlying member method code with example usage: obj.testGaussianizeRagged(input_dtype) and returns: dict