def METHOD_NAME(num_hash_buckets, embedding_dimension):
  """Creates sequential input columns to `RNNEstimator`.
  Args:
    num_hash_buckets: `int`, number of embedding vectors to use.
    embedding_dimension: `int`, size of embedding vectors.
  Returns:
    List of `tf.feature_column` ojects.
  """
  id_col = feature_column.sequence_categorical_column_with_hash_bucket(
      constants.TOKENS, num_hash_buckets, dtype=tf.string)
  features_columns = [tf.feature_column.embedding_column(
      id_col, dimension=embedding_dimension)]
  return features_columns