docstr: ivy.Container static method variant of ivy.poisson. This method
  simply wraps the function, and so the docstring for ivy.poisson also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  lam
      Input container with rate parameter(s) describing the poisson
      distribution(s) to sample.
  shape
      optional container including ints or tuple of ints,
      Output shape for the arrays in the input container.
  device
      device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
      (Default value = None).
  dtype
      output container array data type. If ``dtype`` is ``None``, the output data
      type will be the default floating-point data type. Default ``None``
  seed
      A python integer. Used to create a random seed distribution.
  fill_value
      if lam is negative, fill the output array with this value
      on that specific dimension.
  out
      optional output container, for writing the result to.
  
  Returns
  -------
  ret
      container including the drawn samples.
  
  Examples
  --------
  >>> lam = ivy.Container(a=ivy.array([7,6,5]),                                 b=ivy.array([8,9,4]))
  >>> shape = ivy.Container(a=(2,3), b=(1,1,3))
  >>> ivy.Container.static_poisson(lam, shape=shape)
  {
      a: ivy.array([[5, 4, 6],
                    [12, 4, 5]]),
      b: ivy.array([[[8, 13, 3]]])
  }
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code: def static_poisson(
      lam: ivy.Container,
      *,
      key_chains: Optional[
          Union[List[str], Dict[str, str], ivy.Container]
      ] = None,
      to_apply: Union[bool, ivy.Container] = True,
      prune_unapplied: Union[bool, ivy.Container] = False,
      map_sequences: Union[bool, ivy.Container] = False,
      shape: Optional[
          Union[ivy.Shape, ivy.NativeShape, ivy.Container]
      ] = None,
      device: Optional[
          Union[ivy.Device, ivy.NativeDevice, ivy.Container]
      ] = None,
      dtype: Optional[
          Union[ivy.Dtype, ivy.NativeDtype, ivy.Container]
      ] = None,
      seed: Optional[Union[int, ivy.Container]] = None,
      fill_value: Optional[Union[float, int, ivy.Container]] = 0,
      out: Optional[ivy.Container] = None,
  ) -> ivy.Container:
      """ivy.Container static method variant of ivy.poisson. This method
          simply wraps the function, and so the docstring for ivy.poisson also
          applies to this method with minimal changes.
  
          Parameters
          ----------
          lam
              Input container with rate parameter(s) describing the poisson
              distribution(s) to sample.
          shape
              optional container including ints or tuple of ints,
              Output shape for the arrays in the input container.
          device
              device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
              (Default value = None).
          dtype
              output container array data type. If ``dtype`` is ``None``, the output data
              type will be the default floating-point data type. Default ``None``
          seed
              A python integer. Used to create a random seed distribution.
          fill_value
              if lam is negative, fill the output array with this value
              on that specific dimension.
          out
              optional output container, for writing the result to.
  
          Returns
          -------
          ret
              container including the drawn samples.
  
          Examples
          --------
          >>> lam = ivy.Container(a=ivy.array([7,6,5]), \
                                  b=ivy.array([8,9,4]))
          >>> shape = ivy.Container(a=(2,3), b=(1,1,3))
          >>> ivy.Container.static_poisson(lam, shape=shape)
          {
              a: ivy.array([[5, 4, 6],
                            [12, 4, 5]]),
              b: ivy.array([[[8, 13, 3]]])
          }
          """
      return ContainerBase.cont_multi_map_in_function(
          "poisson",
          lam,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          shape=shape,
          device=device,
          dtype=dtype,
          seed=seed,
          fill_value=fill_value,
          out=out,
      )
  
file_path: ivy/ivy/data_classes/container/experimental/random.py
imports: from typing import Optional, Union, List, Dict
  import ivy
  from ivy.data_classes.container.base import ContainerBase
label_desc: Use image node_id 5 for calling the _ContainerWithRandomExperimental obj's underlying member method code with example usage: obj.static_poisson(lam) and returns: ContainerBase