# tf.sparse.cross_hashed

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/ops/sparse_ops.py#L664-L708) |

Generates hashed sparse cross from a list of sparse and dense tensors.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.sparse.cross_hashed`](https://www.tensorflow.org/api_docs/python/tf/sparse/cross_hashed)

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    tf.sparse.cross_hashed(
        inputs, num_buckets=0, hash_key=None, name=None
    )

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")))

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| ## 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. |

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| ## Returns ||
|---|---|
| A `SparseTensor` of type `int64`. ||

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