# tf.io.deserialize_many_sparse

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/sparse_ops.py#L2345-L2416) |

Deserialize and concatenate `SparseTensors` from a serialized minibatch.

#### View aliases


**Compat aliases for migration**

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

[`tf.compat.v1.deserialize_many_sparse`](https://www.tensorflow.org/api_docs/python/tf/io/deserialize_many_sparse), [`tf.compat.v1.io.deserialize_many_sparse`](https://www.tensorflow.org/api_docs/python/tf/io/deserialize_many_sparse)

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    tf.io.deserialize_many_sparse(
        serialized_sparse, dtype, rank=None, name=None
    )

The input `serialized_sparse` must be a string matrix of shape `[N x 3]` where
`N` is the minibatch size and the rows correspond to packed outputs of
`serialize_sparse`. The ranks of the original `SparseTensor` objects
must all match. When the final `SparseTensor` is created, it has rank one
higher than the ranks of the incoming `SparseTensor` objects (they have been
concatenated along a new row dimension).

The output `SparseTensor` object's shape values for all dimensions but the
first are the max across the input `SparseTensor` objects' shape values
for the corresponding dimensions. Its first shape value is `N`, the minibatch
size.

The input `SparseTensor` objects' indices are assumed ordered in
standard lexicographic order. If this is not the case, after this
step run [`sparse.reorder`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/sparse/reorder) to restore index ordering.

For example, if the serialized input is a `[2, 3]` matrix representing two
original `SparseTensor` objects:

    index = [ 0]
            [10]
            [20]
    values = [1, 2, 3]
    shape = [50]

and

    index = [ 2]
            [10]
    values = [4, 5]
    shape = [30]

then the final deserialized `SparseTensor` will be:

    index = [0  0]
            [0 10]
            [0 20]
            [1  2]
            [1 10]
    values = [1, 2, 3, 4, 5]
    shape = [2 50]

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| ## Args ||
|---|---|
| `serialized_sparse` | 2-D `Tensor` of type `string` of shape `[N, 3]`. The serialized and packed `SparseTensor` objects. |
| `dtype` | The `dtype` of the serialized `SparseTensor` objects. |
| `rank` | (optional) Python int, the rank of the `SparseTensor` objects. |
| `name` | A name prefix for the returned tensors (optional) |

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| ## Returns ||
|---|---|
| A `SparseTensor` representing the deserialized `SparseTensor`s, concatenated along the `SparseTensor`s' first dimension. <br /> All of the serialized `SparseTensor`s must have had the same rank and type. ||

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