code: def testComputeAndApplyApproximateVocabulary(
      self, store_frequency, reserved_tokens
  ):
      input_data = [{"x": "a"}] * 2 + [{"x": "b"}] * 3
      input_metadata = tft.DatasetMetadata.from_feature_spec(
          {"x": tf.io.FixedLenFeature([], tf.string)}
      )
  
      def preprocessing_fn(inputs):
          index = (
              tft.experimental.compute_and_apply_approximate_vocabulary(
                  inputs["x"],
                  top_k=2,
                  file_format=self._VocabFormat(),
                  store_frequency=store_frequency,
                  reserved_tokens=reserved_tokens,
                  num_oov_buckets=1,
              )
          )
          return {"index": index}
  
      offset = len(reserved_tokens) if reserved_tokens else 0
      expected_data = [
          {"index": offset + idx} for idx in [1] * 2 + [0] * 3 + [2]
      ]
  
      self.assertAnalyzeAndTransformResults(
          input_data,
          input_metadata,
          preprocessing_fn,
          expected_data,
          test_data=input_data + [{"x": "c"}],
      )
  
imports: import os
  import apache_beam
  import tensorflow
  import tensorflow_transform
  from tensorflow_transform.beam import analyzer_impls
  from tensorflow_transform.beam import impl
  from tensorflow_transform.beam.tft_beam_io import transform_fn_io
  from tensorflow_transform.beam import tft_unit
  from tensorflow_metadata.proto.v0 import schema_pb2
label_desc: Use image node_id 5 for calling the VocabularyIntegrationTest obj's underlying member method code with example usage: obj.testComputeAndApplyApproximateVocabulary(store_frequency, reserved_tokens) and returns: dict