code: def testGaussianize(self, input_data, output_data, elementwise):
      def preprocessing_fn(inputs):
          x = inputs["x"]
          x_cast = tf.cast(x, tf.as_dtype(input_data.dtype))
          x_gaussianized = tft.scale_to_gaussian(
              x_cast, elementwise=elementwise
          )
          self.assertEqual(
              x_gaussianized.dtype, tf.as_dtype(output_data.dtype)
          )
          return {"x_gaussianized": tf.cast(x_gaussianized, tf.float32)}
  
      input_data_dicts = [{"x": x} for x in input_data]
      expected_data_dicts = [
          {"x_gaussianized": x_gaussianized}
          for x_gaussianized in output_data
      ]
      input_metadata = tft.DatasetMetadata.from_feature_spec(
          {
              "x": tf.io.FixedLenFeature(
                  input_data.shape[1:],
                  tft_unit.canonical_numeric_dtype(
                      tf.as_dtype(input_data.dtype)
                  ),
              ),
          }
      )
      expected_metadata = tft.DatasetMetadata.from_feature_spec(
          {
              "x_gaussianized": tf.io.FixedLenFeature(
                  output_data.shape[1:], tf.float32
              ),
          }
      )
      self.assertAnalyzeAndTransformResults(
          input_data_dicts,
          input_metadata,
          preprocessing_fn,
          expected_data_dicts,
          expected_metadata,
          desired_batch_size=20,
          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 3 for calling the TukeyHHParamsIntegrationTest obj's underlying member method code with example usage: obj.testGaussianize(input_data, output_data, elementwise) and returns: dict