code: def context_feature_columns():
      """Returns context feature names to column definitions."""
      if FLAGS.vocab_path:
          sparse_column = (
              tf.feature_column.categorical_column_with_vocabulary_file(
                  key="query_tokens", vocabulary_file=FLAGS.vocab_path
              )
          )
      else:
          sparse_column = (
              tf.feature_column.categorical_column_with_hash_bucket(
                  key="query_tokens", hash_bucket_size=100
              )
          )
      query_embedding_column = tf.feature_column.embedding_column(
          sparse_column, _EMBEDDING_DIMENSION
      )
      return {"query_tokens": query_embedding_column}
  
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 1 for calling a global function with example usage: context_feature_columns() and returns: dict