code: def example_feature_columns():
      """Returns the example feature columns."""
      if FLAGS.vocab_path:
          sparse_column = (
              tf.feature_column.categorical_column_with_vocabulary_file(
                  key="document_tokens",
                  vocabulary_file=FLAGS.vocab_path,
              )
          )
      else:
          sparse_column = (
              tf.feature_column.categorical_column_with_hash_bucket(
                  key="document_tokens", hash_bucket_size=100
              )
          )
      document_embedding_column = tf.feature_column.embedding_column(
          sparse_column, _EMBEDDING_DIMENSION
      )
      feature_columns = {"document_tokens": document_embedding_column}
      return feature_columns
  
file_path: ranking/tensorflow_ranking/extension/examples/pipeline_example.py
imports: from absl import flags
  import tensorflow
  from tensorflow import estimator
  import tensorflow_ranking
label_desc: Use image node_id 2 for calling a global function with example usage: example_feature_columns() and returns: feature_columns