# 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

`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).

A `Tensor` of type `dtype`.

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