code: def stateless_random_poisson(
      shape, seed, lam, dtype=dtypes.int32, name=None
  ):
      """Outputs deterministic pseudorandom values from a Poisson distribution.
  
      The generated values follow a Poisson distribution with specified rate
      parameter.
  
      This is a stateless version of `tf.random.poisson`: 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, but may change between
      versions of TensorFlow or on non-CPU/GPU hardware.
  
      A slight difference exists in the interpretation of the `shape` parameter
      between `stateless_poisson` and `poisson`: in `poisson`, the `shape` is always
      prepended to the shape of `lam`; whereas in `stateless_poisson` the shape of
      `lam` must match the trailing dimensions of `shape`.
  
      Example:
  
      ```python
      samples = tf.random.stateless_poisson([10, 2], seed=[12, 34], lam=[5, 15])
      # samples has shape [10, 2], where each slice [:, 0] and [:, 1] represents
      # the samples drawn from each distribution
  
      samples = tf.random.stateless_poisson([7, 5, 2], seed=[12, 34], lam=[5, 15])
      # samples has shape [7, 5, 2], where each slice [:, :, 0] and [:, :, 1]
      # represents the 7x5 samples drawn from each of the two distributions
  
      rate = tf.constant([[1.], [3.], [5.]])
      samples = tf.random.stateless_poisson([30, 3, 1], seed=[12, 34], lam=rate)
      # samples has shape [30, 3, 1], with 30 samples each of 3x1 distributions.
      ```
  
      Args:
        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.)
        lam: Tensor. The rate parameter "lambda" of the Poisson distribution. Shape
          must match the rightmost dimensions of `shape`.
        dtype: Dtype of the samples (int or float dtypes are permissible, as samples
          are discrete). Default: int32.
        name: A name for the operation (optional).
  
      Returns:
        samples: A Tensor of the specified shape filled with random Poisson values.
          For each i, each `samples[..., i]` is an independent draw from the Poisson
          distribution with rate `lam[i]`.
      """
      with ops.name_scope(
          name, "stateless_random_poisson", [shape, seed, lam]
      ) as name:
          shape = shape_util.shape_tensor(shape)
          result = gen_stateless_random_ops.stateless_random_poisson(
              shape, seed=seed, lam=lam, dtype=dtype
          )
          shape_util.maybe_set_static_shape(result, shape)
          return result
  
imports: import numpy
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import array_ops_stack
  from tensorflow.python.ops import gen_random_index_shuffle_ops
  from tensorflow.python.ops import gen_stateless_random_ops
  from tensorflow.python.ops import gen_stateless_random_ops_v2
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import random_ops_util
  from tensorflow.python.ops import shape_util
  from tensorflow.python.util import deprecation
  from tensorflow.python.util import dispatch
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 8 for calling a global function with example usage: stateless_random_poisson(shape, seed, lam, dtype, name) and returns: result