# tf.random.poisson

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

``````tf.random.poisson(
shape, lam, dtype=tf.dtypes.float32, seed=None, name=None
)
``````

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

#### Example:

``````samples = tf.random.poisson(, [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
``````

#### Args:

• `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.

#### Returns:

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