parameters: (column_name,
                                     hash_bucket_size,
                                     combiner="sum")
return_statement: return _SparseColumnHashed(column_name, hash_bucket_size, combiner)
docstring: Creates a _SparseColumn with hashed bucket configuration.
  
    Use this when your sparse features are in string format, but you don't have a
    vocab file that maps each string to an integer ID.
    output_id = Hash(input_feature_string) % bucket_size
  
    Args:
      column_name: A string defining sparse column name.
      hash_bucket_size: An int that is > 1. The number of buckets.
      combiner: A string specifying how to reduce if the sparse column is
        multivalent. Currently "mean", "sqrtn" and "sum" are supported, with
        "sum" the default:
          * "sum": do not normalize features in the column
          * "mean": do l1 normalization on features in the column
          * "sqrtn": do l2 normalization on features in the column
        For more information: `tf.embedding_lookup_sparse`.
  
    Returns:
      A _SparseColumn with hashed bucket configuration
  
    Raises:
      ValueError: hash_bucket_size is not greater than 2.
docstring_summary: Creates a _SparseColumn with hashed bucket configuration.
docstring_tokens: Creates a _SparseColumn with hashed bucket configuration .
function: def sparse_column_with_hash_bucket(column_name,
                                     hash_bucket_size,
                                     combiner="sum"):
    """Creates a _SparseColumn with hashed bucket configuration.
  
    Use this when your sparse features are in string format, but you don't have a
    vocab file that maps each string to an integer ID.
    output_id = Hash(input_feature_string) % bucket_size
  
    Args:
      column_name: A string defining sparse column name.
      hash_bucket_size: An int that is > 1. The number of buckets.
      combiner: A string specifying how to reduce if the sparse column is
        multivalent. Currently "mean", "sqrtn" and "sum" are supported, with
        "sum" the default:
          * "sum": do not normalize features in the column
          * "mean": do l1 normalization on features in the column
          * "sqrtn": do l2 normalization on features in the column
        For more information: `tf.embedding_lookup_sparse`.
  
    Returns:
      A _SparseColumn with hashed bucket configuration
  
    Raises:
      ValueError: hash_bucket_size is not greater than 2.
    """
    return _SparseColumnHashed(column_name, hash_bucket_size, combiner)
function_tokens: def sparse_column_with_hash_bucket ( column_name , hash_bucket_size , combiner = "sum" ) : return _SparseColumnHashed ( column_name , hash_bucket_size , combiner )