code: def tfidf(
      x: tf.SparseTensor,
      vocab_size: int,
      smooth: bool = True,
      name: Optional[str] = None,
  ) -> Tuple[tf.SparseTensor, tf.SparseTensor]:
      # pyformat: disable
      """Maps the terms in x to their term frequency * inverse document frequency.
  
      The term frequency of a term in a document is calculated as
      (count of term in document) / (document size)
  
      The inverse document frequency of a term is, by default, calculated as
      1 + log((corpus size + 1) / (count of documents containing term + 1)).
  
  
      Example usage:
  
      >>> def preprocessing_fn(inputs):
      ...   integerized = tft.compute_and_apply_vocabulary(inputs['x'])
      ...   vocab_size = tft.get_num_buckets_for_transformed_feature(integerized)
      ...   vocab_index, tfidf_weight = tft.tfidf(integerized, vocab_size)
      ...   return {
      ...      'index': vocab_index,
      ...      'tf_idf': tfidf_weight,
      ...      'integerized': integerized,
      ...   }
      >>> raw_data = [dict(x=["I", "like", "pie", "pie", "pie"]),
      ...             dict(x=["yum", "yum", "pie"])]
      >>> feature_spec = dict(x=tf.io.VarLenFeature(tf.string))
      >>> raw_data_metadata = tft.DatasetMetadata.from_feature_spec(feature_spec)
      >>> with tft_beam.Context(temp_dir=tempfile.mkdtemp()):
      ...   transformed_dataset, transform_fn = (
      ...       (raw_data, raw_data_metadata)
      ...       | tft_beam.AnalyzeAndTransformDataset(preprocessing_fn))
      >>> transformed_data, transformed_metadata = transformed_dataset
      >>> transformed_data
      [{'index': array([0, 2, 3]), 'integerized': array([3, 2, 0, 0, 0]),
        'tf_idf': array([0.6, 0.28109303, 0.28109303], dtype=float32)},
       {'index': array([0, 1]), 'integerized': array([1, 1, 0]),
        'tf_idf': array([0.33333334, 0.9369768 ], dtype=float32)}]
  
        ```
        example strings: [["I", "like", "pie", "pie", "pie"], ["yum", "yum", "pie]]
        in: SparseTensor(indices=[[0, 0], [0, 1], [0, 2], [0, 3], [0, 4],
                                  [1, 0], [1, 1], [1, 2]],
                         values=[1, 2, 0, 0, 0, 3, 3, 0])
        out: SparseTensor(indices=[[0, 0], [0, 1], [0, 2], [1, 0], [1, 1]],
                          values=[1, 2, 0, 3, 0])
             SparseTensor(indices=[[0, 0], [0, 1], [0, 2], [1, 0], [1, 1]],
                          values=[(1/5)*(log(3/2)+1), (1/5)*(log(3/2)+1), (3/5),
                                  (2/3)*(log(3/2)+1), (1/3)]
        ```
  
        NOTE: the first doc's duplicate "pie" strings have been combined to
        one output, as have the second doc's duplicate "yum" strings.
  
      Args:
        x: A 2D `SparseTensor` representing int64 values (most likely that are the
            result of calling `compute_and_apply_vocabulary` on a tokenized string).
        vocab_size: An int - the count of vocab used to turn the string into int64s
            including any OOV buckets.
        smooth: A bool indicating if the inverse document frequency should be
            smoothed. If True, which is the default, then the idf is calculated as
            1 + log((corpus size + 1) / (document frequency of term + 1)).
            Otherwise, the idf is
            1 +log((corpus size) / (document frequency of term)), which could
            result in a division by zero error.
        name: (Optional) A name for this operation.
  
      Returns:
        Two `SparseTensor`s with indices [index_in_batch, index_in_bag_of_words].
        The first has values vocab_index, which is taken from input `x`.
        The second has values tfidf_weight.
  
      Raises:
        ValueError if `x` does not have 2 dimensions.
      """
      # pyformat: enable
      if x.get_shape().ndims != 2:
          raise ValueError(
              "tft.tfidf requires a 2D SparseTensor input. "
              "Input had {} dimensions.".format(x.get_shape().ndims)
          )
  
      with tf.compat.v1.name_scope(name, "tfidf"):
          cleaned_input = tf_utils.to_vocab_range(x, vocab_size)
  
          term_frequencies = _to_term_frequency(
              cleaned_input, vocab_size
          )
  
          count_docs_with_term_column = _count_docs_with_term(
              term_frequencies
          )
          # Expand dims to get around the min_tensor_rank checks
          sizes = tf.expand_dims(tf.shape(input=cleaned_input)[0], 0)
          # [batch, vocab] - tfidf
          tfidfs = _to_tfidf(
              term_frequencies,
              analyzers.sum(
                  count_docs_with_term_column,
                  reduce_instance_dims=False,
              ),
              analyzers.sum(sizes),
              smooth,
          )
          return _split_tfidfs_to_outputs(tfidfs)
  
imports: import os
  from typing import Any, Callable, Iterable, Optional, Tuple, Union
  import tensorflow
  from tensorflow_transform import analyzers
  from tensorflow_transform import common
  from tensorflow_transform import common_types
  from tensorflow_transform import gaussianization
  from tensorflow_transform import schema_inference
  from tensorflow_transform import tf_utils
  from tfx_bsl.types import tfx_namedtuple
label_desc: Use image node_id 13 for calling a global function with example usage: tfidf(x, vocab_size, smooth, name) and returns: _split_tfidfs_to_outputs