docstr: ivy.Container instance method variant of ivy.multinomial. This
  method simply wraps the function, and so the docstring for
  ivy.multinomial also applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      The unnormalized probabilities for all elements in population,
      default is uniform *[batch_shape, population_size]*
  population_size
      The size of the population from which to draw samples.
  num_samples
      Number of independent samples to draw from the population.
  batch_size
      Number of tensors to generate. Default is 1.
  replace
      Whether to replace samples once they've been drawn. Default is ``True``.
  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)
  seed
      A python integer. Used to create a random seed distribution
  out
      optional output array, for writing the result to. It must have a shape
      that the inputs broadcast to.
  
  Returns
  -------
  ret
      Drawn samples from the parameterized normal distribution.
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code: def multinomial(
      self: ivy.Container,
      population_size: Union[int, ivy.Container],
      num_samples: Union[int, ivy.Container],
      /,
      *,
      batch_size: Union[int, ivy.Container] = 1,
      replace: Union[bool, ivy.Container] = True,
      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[ivy.Device, ivy.NativeDevice, ivy.Container]
      ] = None,
      seed: Optional[Union[int, ivy.Container]] = None,
      out: Optional[ivy.Container] = None,
  ) -> ivy.Container:
      """ivy.Container instance method variant of ivy.multinomial. This
      method simply wraps the function, and so the docstring for
      ivy.multinomial also applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          The unnormalized probabilities for all elements in population,
          default is uniform *[batch_shape, population_size]*
      population_size
          The size of the population from which to draw samples.
      num_samples
          Number of independent samples to draw from the population.
      batch_size
          Number of tensors to generate. Default is 1.
      replace
          Whether to replace samples once they've been drawn. Default is ``True``.
      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)
      seed
          A python integer. Used to create a random seed distribution
      out
          optional output array, for writing the result to. It must have a shape
          that the inputs broadcast to.
  
      Returns
      -------
      ret
          Drawn samples from the parameterized normal distribution.
      """
      return self._static_multinomial(
          population_size,
          num_samples,
          batch_size=batch_size,
          probs=self,
          replace=replace,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          device=device,
          seed=seed,
          out=out,
      )
  
file_path: ivy/ivy/data_classes/container/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 6 for calling the _ContainerWithRandom obj's underlying member method code with example usage: obj.multinomial() and returns: self