code: def fold_in(seed, data, alg="auto_select"):
      """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)>
  
      Args:
        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.
        alg: The RNG algorithm used to generate the random numbers. See
          `tf.random.stateless_uniform` for a detailed explanation.
  
      Returns:
        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`).
      """
      data = ops.convert_to_tensor(data)
      seed1 = stateless_random_uniform(
          shape=[],
          seed=seed,
          dtype=data.dtype,
          minval=None,
          maxval=None,
          alg=alg,
      )
      return array_ops_stack.stack([seed1, data])
  
imports: import numpy
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import gen_random_index_shuffle_ops
  from tensorflow.python.ops import gen_stateless_random_ops
  from tensorflow.python.ops import gen_stateless_random_ops_v2
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops_util
  from tensorflow.python.ops import shape_util
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import dispatch
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 2 for calling a global function with example usage: fold_in(seed, data, alg) and returns: array_ops_stack