parameters: (
      column_name, keys, default_value=-1, combiner="sum", dtype=dtypes.string)
return_statement: return _SparseColumnKeys(
        column_name,
        lookup_config=_SparseIdLookupConfig(
            keys=keys, vocab_size=len(keys), default_value=default_value),
        combiner=combiner,
        dtype=dtype)
docstring: Creates a _SparseColumn with keys.
  
    Look up logic is as follows:
    lookup_id = index_of_feature_in_keys if feature in keys else default_value
  
    Args:
      column_name: A string defining sparse column name.
      keys: A list or tuple defining vocabulary. Must be castable to `dtype`.
      default_value: The value to use for out-of-vocabulary feature values.
        Default is -1.
      combiner: A string specifying how to reduce if the sparse column is
        multivalent. Currently "mean", "sqrtn" and "sum" are supported, with "sum"
        the default. "sqrtn" often achieves good accuracy, in particular with
        bag-of-words columns.
          * "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`.
      dtype: Type of features. Only integer and string are supported.
  
    Returns:
      A _SparseColumnKeys with keys configuration.
docstring_summary: Creates a _SparseColumn with keys.
docstring_tokens: Creates a _SparseColumn with keys .
function: def sparse_column_with_keys(
      column_name, keys, default_value=-1, combiner="sum", dtype=dtypes.string):
    """Creates a _SparseColumn with keys.
  
    Look up logic is as follows:
    lookup_id = index_of_feature_in_keys if feature in keys else default_value
  
    Args:
      column_name: A string defining sparse column name.
      keys: A list or tuple defining vocabulary. Must be castable to `dtype`.
      default_value: The value to use for out-of-vocabulary feature values.
        Default is -1.
      combiner: A string specifying how to reduce if the sparse column is
        multivalent. Currently "mean", "sqrtn" and "sum" are supported, with "sum"
        the default. "sqrtn" often achieves good accuracy, in particular with
        bag-of-words columns.
          * "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`.
      dtype: Type of features. Only integer and string are supported.
  
    Returns:
      A _SparseColumnKeys with keys configuration.
    """
    keys = tuple(keys)
    return _SparseColumnKeys(
        column_name,
        lookup_config=_SparseIdLookupConfig(
            keys=keys, vocab_size=len(keys), default_value=default_value),
        combiner=combiner,
        dtype=dtype)
function_tokens: def sparse_column_with_keys ( column_name , keys , default_value = - 1 , combiner = "sum" , dtype = dtypes . string ) : keys = tuple ( keys ) return _SparseColumnKeys ( column_name , lookup_config = _SparseIdLookupConfig ( keys = keys , vocab_size = len ( keys ) , default_value = default_value ) , combiner = combiner , dtype = dtype )