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Outputs deterministic pseudorandom values from a binomial distribution.

The generated values follow a binomial distribution with specified count and probability of success parameters.

This is a stateless version of tf.random.Generator.binomial: if run twice with the same seeds and shapes, it will produce the same pseudorandom numbers. The output is consistent across multiple runs on the same hardware (and between CPU and GPU), but may change between versions of TensorFlow or on non-CPU/GPU hardware.


counts = [10., 20.]
# Probability of success.
probs = [0.8]

binomial_samples = tf.random.stateless_binomial(
    shape=[2], seed=[123, 456], counts=counts, probs=probs)

counts = ... # Shape [3, 1, 2]
probs = ...  # Shape [1, 4, 2]
shape = [3, 4, 3, 4, 2]
# Sample shape will be [3, 4, 3, 4, 2]
binomial_samples = tf.random.stateless_binomial(
    shape=shape, seed=[123, 456], counts=counts, probs=probs)

shape A 1-D integer Tensor or Python array. The shape of the output tensor.
seed A shape [2] Tensor, the seed to the random number generator. Must have dtype int32 or int64. (When using XLA, only int32 is allowed.)
counts Tensor. The counts of the binomial distribution. Must be broadcastable with probs, and broadcastable with the rightmost dimensions of shape.
probs Tensor. The probability of success for the binomial distribution. Must be broadcastable with counts and broadcastable with the rightmost dimensions of shape.
output_dtype The type of the output. Default: tf.int32
name A name for the operation (optional).

samples A Tensor of the specified shape filled with random binomial values. For each i, each samples[..., i] is an independent draw from the binomial distribution on counts[i] trials with probability of success probs[i].