```
tf.data.experimental.sample_from_datasets(
datasets,
weights=None,
seed=None
)
```

Defined in `tensorflow/python/data/experimental/ops/interleave_ops.py`

.

Samples elements at random from the datasets in `datasets`

.

#### Args:

: A list of`datasets`

`tf.data.Dataset`

objects with compatible structure.: (Optional.) A list of`weights`

`len(datasets)`

floating-point values where`weights[i]`

represents the probability with which an element should be sampled from`datasets[i]`

, or a`tf.data.Dataset`

object where each element is such a list. Defaults to a uniform distribution across`datasets`

.: (Optional.) A`seed`

`tf.int64`

scalar`tf.Tensor`

, representing the random seed that will be used to create the distribution. See`tf.set_random_seed`

for behavior.

#### Returns:

A dataset that interleaves elements from `datasets`

at random, according to
`weights`

if provided, otherwise with uniform probability.

#### Raises:

: If the`TypeError`

`datasets`

or`weights`

arguments have the wrong type.: If the`ValueError`

`weights`

argument is specified and does not match the length of the`datasets`

element.