/TensorFlow 2.4

tf.raw_ops.RandomPoissonV2

Outputs random values from the Poisson distribution(s) described by rate.

This op uses two algorithms, depending on rate. If rate >= 10, then the algorithm by Hormann is used to acquire samples via transformation-rejection. See http://www.sciencedirect.com/science/article/pii/0167668793909974

Otherwise, Knuth's algorithm is used to acquire samples via multiplying uniform random variables. See Donald E. Knuth (1969). Seminumerical Algorithms. The Art of Computer Programming, Volume 2. Addison Wesley

Args
`shape` A `Tensor`. Must be one of the following types: `int32`, `int64`. 1-D integer tensor. Shape of independent samples to draw from each distribution described by the shape parameters given in rate.
`rate` A `Tensor`. Must be one of the following types: `half`, `float32`, `float64`, `int32`, `int64`. A tensor in which each scalar is a "rate" parameter describing the associated poisson distribution.
`seed` An optional `int`. Defaults to `0`. If either `seed` or `seed2` are set to be non-zero, the random number generator is seeded by the given seed. Otherwise, it is seeded by a random seed.
`seed2` An optional `int`. Defaults to `0`. A second seed to avoid seed collision.
`dtype` An optional `tf.DType` from: `tf.half, tf.float32, tf.float64, tf.int32, tf.int64`. Defaults to `tf.int64`.
`name` A name for the operation (optional).
Returns
A `Tensor` of type `dtype`.