tf.raw_ops.ShuffleAndRepeatDataset

Creates a dataset that shuffles and repeats elements from input_dataset

Compat aliases for migration

tf.compat.v1.raw_ops.ShuffleAndRepeatDataset

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

pseudorandomly.

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` or list of `ints`) that has length `>= 1`.
`name`	A name for the operation (optional).

Returns
A `Tensor` of type `variant`.

Args