# tf.compat.v1.serialize_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#L2213-L2240) |

Serialize `N`-minibatch `SparseTensor` into an `[N, 3]` `Tensor`.

#### View aliases


**Compat aliases for migration**

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

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

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    tf.compat.v1.serialize_many_sparse(
        sp_input,
        name=None,
        out_type=https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/dtypes#string
    )

The `SparseTensor` must have rank `R` greater than 1, and the first dimension
is treated as the minibatch dimension. Elements of the `SparseTensor`
must be sorted in increasing order of this first dimension. The serialized
`SparseTensor` objects going into each row of the output `Tensor` will have
rank `R-1`.

The minibatch size `N` is extracted from `sparse_shape[0]`.

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| ## Args ||
|---|---|
| `sp_input` | The input rank `R` `SparseTensor`. |
| `name` | A name prefix for the returned tensors (optional). |
| `out_type` | The `dtype` to use for serialization. |

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| ## Returns ||
|---|---|
| A matrix (2-D `Tensor`) with `N` rows and `3` columns. Each column represents serialized `SparseTensor`'s indices, values, and shape (respectively). ||

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| ## Raises ||
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| `TypeError` | If `sp_input` is not a `SparseTensor`. |

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