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# tf.data.experimental.sample_from_datasets

Samples elements at random from the datasets in `datasets`.

### Used in the notebooks

Used in the guide Used in the tutorials

Creates a dataset by interleaving elements of `datasets` with the `weight[i]` probability of picking an element from dataset `i`. For example, suppose we have 2 datasets:

``````dataset1 = tf.data.Dataset.range(0, 3)
dataset2 = tf.data.Dataset.range(100, 103)
``````

Suppose also that we sample from these 2 datasets with the following weights:

``````sample_dataset = tf.data.experimental.sample_from_datasets(
[dataset1, dataset2], weights=[0.5, 0.5])
``````

One possible outcome of elements in sample_dataset is:

``````print(list(sample_dataset.as_numpy_iterator()))
# [100, 0, 1, 101, 2, 102]
``````

`datasets` A list of `tf.data.Dataset` objects with compatible structure.
`weights` (Optional.) A list of `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`.
`seed` (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the random seed that will be used to create the distribution. See `tf.random.set_seed` for behavior.

A dataset that interleaves elements from `datasets` at random, according to `weights` if provided, otherwise with uniform probability.

`TypeError` If the `datasets` or `weights` arguments have the wrong type.
`ValueError` If the `weights` argument is specified and does not match the length of the `datasets` element.

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