docstr: ivy.Container static method variant of ivy.gamma. This method simply
  wraps the function, and so the docstring for ivy.gamma also applies to
  this method with minimal changes.
  
  Parameters
  ----------
  alpha
      First parameter of the distribution.
  beta
      Second parameter of the distribution.
  shape
      If the given shape is, e.g '(m, n, k)', then 'm * n * k' samples are drawn.
      (Default value = 'None', where 'ivy.shape(logits)' samples are drawn)
  key_chains
      The key-chains to apply or not apply the method to. Default is ``None``.
  to_apply
      If True, the method will be applied to key_chains, otherwise key_chains
      will be skipped. Default is ``True``.
  prune_unapplied
      Whether to prune key_chains for which the function was not applied.
      Default is ``False``.
  map_sequences
      Whether to also map method to sequences (lists, tuples).
      Default is ``False``.
  device
      device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
      (Default value = None).
  
  dtype
      output array data type. If ``dtype`` is ``None``, the output array data
      type will be the default floating-point data type. Default ``None``
  seed
      A python integer. Used to create a random seed distribution
  
  out
      Optional output container, for writing the result to. It must have a shape
      that the inputs broadcast to.
  
  Returns
  -------
  ret
      Drawn samples from the parameterized gamma distribution with the shape of
      the input Container.
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code: def static_gamma(
      alpha: ivy.Container,
      beta: Union[
          int, float, ivy.Container, ivy.Array, ivy.NativeArray
      ],
      /,
      *,
      shape: Optional[
          Union[ivy.Shape, ivy.NativeShape, ivy.Container]
      ] = None,
      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,
      device: Optional[Union[str, ivy.Container]] = None,
      dtype: Optional[Union[str, ivy.Container]] = None,
      seed: Optional[Union[int, ivy.Container]] = None,
      out: Optional[ivy.Container] = None,
  ):
      """ivy.Container static method variant of ivy.gamma. This method simply
      wraps the function, and so the docstring for ivy.gamma also applies to
      this method with minimal changes.
  
      Parameters
      ----------
      alpha
          First parameter of the distribution.
      beta
          Second parameter of the distribution.
      shape
          If the given shape is, e.g '(m, n, k)', then 'm * n * k' samples are drawn.
          (Default value = 'None', where 'ivy.shape(logits)' samples are drawn)
      key_chains
          The key-chains to apply or not apply the method to. Default is ``None``.
      to_apply
          If True, the method will be applied to key_chains, otherwise key_chains
          will be skipped. Default is ``True``.
      prune_unapplied
          Whether to prune key_chains for which the function was not applied.
          Default is ``False``.
      map_sequences
          Whether to also map method to sequences (lists, tuples).
          Default is ``False``.
      device
          device on which to create the array 'cuda:0', 'cuda:1', 'cpu' etc.
          (Default value = None).
  
      dtype
          output array data type. If ``dtype`` is ``None``, the output array data
          type will be the default floating-point data type. Default ``None``
      seed
          A python integer. Used to create a random seed distribution
  
      out
          Optional output container, for writing the result to. It must have a shape
          that the inputs broadcast to.
  
      Returns
      -------
      ret
          Drawn samples from the parameterized gamma distribution with the shape of
          the input Container.
      """
      return ContainerBase.cont_multi_map_in_function(
          "gamma",
          alpha,
          beta,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          shape=shape,
          device=device,
          dtype=dtype,
          seed=seed,
          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 9 for calling the _ContainerWithRandomExperimental obj's underlying member method code with example usage: obj.static_gamma() and returns: ContainerBase