parameters: (inputs, num_buckets=0, hash_key=None, name=None)
return_statement: return _sparse_cross_internal(
        inputs=inputs,
        hashed_output=True,
        num_buckets=num_buckets,
        hash_key=hash_key,
        name=name)
docstring: Generates hashed sparse cross from a list of sparse and dense tensors.
  
    For example, if the inputs are
    * inputs[0]: SparseTensor with shape = [2, 2]
      [0, 0]: "a"
      [1, 0]: "b"
      [1, 1]: "c"
    * inputs[1]: SparseTensor with shape = [2, 1]
      [0, 0]: "d"
      [1, 0]: "e"
    * inputs[2]: Tensor [["f"], ["g"]]
  
    then the output will be:
      shape = [2, 2]
      [0, 0]: FingerprintCat64(
                  Fingerprint64("f"), FingerprintCat64(
                      Fingerprint64("d"), Fingerprint64("a")))
      [1, 0]: FingerprintCat64(
                  Fingerprint64("g"), FingerprintCat64(
                      Fingerprint64("e"), Fingerprint64("b")))
      [1, 1]: FingerprintCat64(
                  Fingerprint64("g"), FingerprintCat64(
                      Fingerprint64("e"), Fingerprint64("c")))
  
    Args:
      inputs: An iterable of `Tensor` or `SparseTensor`.
      num_buckets: An `int` that is `>= 0`.
        output = hashed_value%num_buckets if num_buckets > 0 else hashed_value.
      hash_key: Integer hash_key that will be used by the `FingerprintCat64`
        function. If not given, will use a default key.
      name: Optional name for the op.
  
    Returns:
      A `SparseTensor` of type `int64`.
docstring_summary: Generates hashed sparse cross from a list of sparse and dense tensors.
docstring_tokens: Generates hashed sparse cross from a list of sparse and dense tensors .
function: def _sparse_cross_hashed(inputs, num_buckets=0, hash_key=None, name=None):
    """Generates hashed sparse cross from a list of sparse and dense tensors.
  
    For example, if the inputs are
    * inputs[0]: SparseTensor with shape = [2, 2]
      [0, 0]: "a"
      [1, 0]: "b"
      [1, 1]: "c"
    * inputs[1]: SparseTensor with shape = [2, 1]
      [0, 0]: "d"
      [1, 0]: "e"
    * inputs[2]: Tensor [["f"], ["g"]]
  
    then the output will be:
      shape = [2, 2]
      [0, 0]: FingerprintCat64(
                  Fingerprint64("f"), FingerprintCat64(
                      Fingerprint64("d"), Fingerprint64("a")))
      [1, 0]: FingerprintCat64(
                  Fingerprint64("g"), FingerprintCat64(
                      Fingerprint64("e"), Fingerprint64("b")))
      [1, 1]: FingerprintCat64(
                  Fingerprint64("g"), FingerprintCat64(
                      Fingerprint64("e"), Fingerprint64("c")))
  
    Args:
      inputs: An iterable of `Tensor` or `SparseTensor`.
      num_buckets: An `int` that is `>= 0`.
        output = hashed_value%num_buckets if num_buckets > 0 else hashed_value.
      hash_key: Integer hash_key that will be used by the `FingerprintCat64`
        function. If not given, will use a default key.
      name: Optional name for the op.
  
    Returns:
      A `SparseTensor` of type `int64`.
    """
    return _sparse_cross_internal(
        inputs=inputs,
        hashed_output=True,
        num_buckets=num_buckets,
        hash_key=hash_key,
        name=name)
function_tokens: def _sparse_cross_hashed ( inputs , num_buckets = 0 , hash_key = None , name = None ) : return _sparse_cross_internal ( inputs = inputs , hashed_output = True , num_buckets = num_buckets , hash_key = hash_key , name = name )