# tf.compat.v2.data.experimental.sample_from_datasets

Samples elements at random from the datasets in `datasets`

.

tf.compat.v2.data.experimental.sample_from_datasets(
datasets, weights=None, seed=None
)

Args |

`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.compat.v1.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 |

`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. |