docstr: ivy.Container instance method variant of ivy.beta. This method
  simply wraps the function, and so the docstring for ivy.beta also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      Input container. Should have a numeric data type.
  alpha
      The alpha parameter of the distribution.
  beta
      The beta parameter of the distribution.
  shape
      The shape of the output array. Default is ``None``.
  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
      The device to place the output array on. Default is ``None``.
  dtype
      The data type of the output array. Default is ``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
      A container object, with values drawn from the beta distribution.
docstr_tok: ["ivy.Container","instance","method","variant","of","ivy.beta",".","This","method","simply","wraps","the","function",",","and","so","the","docstring","for","ivy.beta","also","applies","to","this","method","with","minimal","changes",".","Parameters","--","--","--","--","--","self","Input","container",".","Should","have","a","numeric","data","type",".","alpha","The","alpha","parameter","of","the","distribution",".","beta","The","beta","parameter","of","the","distribution",".","shape","The","shape","of","the","output","array",".","Default","is","``","None","``",".","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","The","device","to","place","the","output","array","on",".","Default","is","``","None","``",".","dtype","The","data","type","of","the","output","array",".","Default","is","``","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","A","container","object",",","with","values","drawn","from","the","beta","distribution","."]
code: def beta(
      self: 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:
      """ivy.Container instance method variant of ivy.beta. This method
      simply wraps the function, and so the docstring for ivy.beta also
      applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          Input container. Should have a numeric data type.
      alpha
          The alpha parameter of the distribution.
      beta
          The beta parameter of the distribution.
      shape
          The shape of the output array. Default is ``None``.
      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
          The device to place the output array on. Default is ``None``.
      dtype
          The data type of the output array. Default is ``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
          A container object, with values drawn from the beta distribution.
      """
      return self.static_beta(
          self,
          beta,
          shape=shape,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          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 4 for calling the _ContainerWithRandomExperimental obj's underlying member method code with example usage: obj.beta() and returns: self