(Optional.) A list or Tensor of len(datasets) floating-point
values where weights[i] represents the probability to sample from
datasets[i], or a tf.data.Dataset object where each element is such a
list. Defaults to a uniform distribution across datasets.
If True, sampling stops if it encounters an empty
dataset. If False, it skips empty datasets. It is recommended to set it
to True. Otherwise, the distribution of samples starts off as the user
intends, but may change as input datasets become empty. This can be
difficult to detect since the dataset starts off looking correct. Default
to False for backward compatibility.
A dataset that interleaves elements from datasets at random, according to
weights if provided, otherwise with uniform probability.
If the datasets or weights arguments have the wrong type.
If datasets is empty, or
If weights is specified and does not match the length of datasets.