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tf.random.experimental.stateless_fold_in

Folds in data to an RNG seed to form a new RNG seed.

For example, in a distributed-training setting, suppose we have a master seed and a replica ID. We want to fold the replica ID into the master seed to form a "replica seed" to be used by that replica later on, so that different replicas will generate different random numbers but the reproducibility of the whole system can still be controlled by the master seed:

master_seed = [1, 2]
replica_id = 3
replica_seed = tf.random.experimental.stateless_fold_in(
  master_seed, replica_id)
print(replica_seed)
tf.Tensor([1105988140          3], shape=(2,), dtype=int32)
tf.random.stateless_normal(shape=[3], seed=replica_seed)
<tf.Tensor: shape=(3,), dtype=float32, numpy=array([0.03197195, 0.8979765 ,
0.13253039], dtype=float32)>

seed an RNG seed (a tensor with shape [2] and dtype int32 or int64). (When using XLA, only int32 is allowed.)
data an int32 or int64 scalar representing data to be folded in to the seed.

A new RNG seed that is a deterministic function of the inputs and is statistically safe for producing a stream of new pseudo-random values. It will have the same dtype as data (if data doesn't have an explict dtype, the dtype will be determined by tf.convert_to_tensor).