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# tf.random.poisson

Draws `shape` samples from each of the given Poisson distribution(s).

`lam` is the rate parameter describing the distribution(s).

#### Example:

``````samples = tf.random.poisson([10], [0.5, 1.5])
# samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents
# the samples drawn from each distribution

samples = tf.random.poisson([7, 5], [12.2, 3.3])
# samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1]
# represents the 7x5 samples drawn from each of the two distributions
``````

`shape` A 1-D integer Tensor or Python array. The shape of the output samples to be drawn per "rate"-parameterized distribution.
`lam` A Tensor or Python value or N-D array of type `dtype`. `lam` provides the rate parameter(s) describing the poisson distribution(s) to sample.
`dtype` The type of the output: `float16`, `float32`, `float64`, `int32` or `int64`.
`seed` A Python integer. Used to create a random seed for the distributions. See `tf.compat.v1.set_random_seed` for behavior.
`name` Optional name for the operation.

`samples` a `Tensor` of shape `tf.concat([shape, tf.shape(lam)], axis=0)` with values of type `dtype`.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]