# tf.raw_ops.ShuffleAndRepeatDataset

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Creates a dataset that shuffles and repeats elements from `input_dataset`

#### View aliases


**Compat aliases for migration**

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

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

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    tf.raw_ops.ShuffleAndRepeatDataset(
        input_dataset,
        buffer_size,
        seed,
        seed2,
        count,
        output_types,
        output_shapes,
        reshuffle_each_iteration=True,
        metadata='',
        name=None
    )

pseudorandomly.

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| ## Args ||
|---|---|
| `input_dataset` | A `Tensor` of type `variant`. |
| `buffer_size` | A `Tensor` of type `int64`. The number of output elements to buffer in an iterator over this dataset. Compare with the `min_after_dequeue` attr when creating a `RandomShuffleQueue`. |
| `seed` | A `Tensor` of type `int64`. A scalar seed for the random number generator. If either `seed` or `seed2` is set to be non-zero, the random number generator is seeded by the given seed. Otherwise, a random seed is used. |
| `seed2` | A `Tensor` of type `int64`. A second scalar seed to avoid seed collision. |
| `count` | A `Tensor` of type `int64`. A scalar representing the number of times the underlying dataset should be repeated. The default is `-1`, which results in infinite repetition. |
| `output_types` | A list of `tf.DTypes` that has length `>= 1`. |
| `output_shapes` | A list of shapes (each a [`tf.TensorShape`](https://www.tensorflow.org/versions/r2.13/api_docs/python/tf/TensorShape) or list of `ints`) that has length `>= 1`. |
| `reshuffle_each_iteration` | An optional `bool`. Defaults to `True`. |
| `metadata` | An optional `string`. Defaults to `""`. |
| `name` | A name for the operation (optional). |

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| ## Returns ||
|---|---|
| A `Tensor` of type `variant`. ||

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