docstr: Parameters
  ----------
  probs
       An N-D Array representing the probability of a 1 event.
       Each entry in the Array parameterizes an independent Bernoulli
       distribution. Only one of logits or probs should be passed in
  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``.
  logits
      An N-D Array representing the log-odds of a 1 event.
      Each entry in the Array parameterizes an independent Bernoulli
      distribution where the probability of an event is sigmoid
      (logits). Only one of logits or probs should be passed in.
  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)
  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
      Drawn samples from the Bernoulli distribution
docstr_tok: ["Parameters","--","--","--","--","--","probs","An","N-D","Array","representing","the","probability","of","a","1","event",".","Each","entry","in","the","Array","parameterizes","an","independent","Bernoulli","distribution",".","Only","one","of","logits","or","probs","should","be","passed","in","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","``",".","logits","An","N-D","Array","representing","the","log-odds","of","a","1","event",".","Each","entry","in","the","Array","parameterizes","an","independent","Bernoulli","distribution","where","the","probability","of","an","event","is","sigmoid","(","logits",")",".","Only","one","of","logits","or","probs","should","be","passed","in",".","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",")","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","Drawn","samples","from","the","Bernoulli","distribution"]
code: def static_bernoulli(
      probs: 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,
      logits: Optional[
          Union[float, ivy.Array, ivy.NativeArray, ivy.Container]
      ] = None,
      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,
      out: Optional[Union[ivy.Array, ivy.Container]] = None,
  ) -> ivy.Container:
      """
  
      Parameters
      ----------
      probs
           An N-D Array representing the probability of a 1 event.
           Each entry in the Array parameterizes an independent Bernoulli
           distribution. Only one of logits or probs should be passed in
      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``.
      logits
          An N-D Array representing the log-odds of a 1 event.
          Each entry in the Array parameterizes an independent Bernoulli
          distribution where the probability of an event is sigmoid
          (logits). Only one of logits or probs should be passed in.
      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)
      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
          Drawn samples from the Bernoulli distribution
  
      """
      return ContainerBase.cont_multi_map_in_function(
          "bernoulli",
          probs,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          logits=logits,
          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 7 for calling the _ContainerWithRandomExperimental obj's underlying member method code with example usage: obj.static_bernoulli(probs) and returns: ContainerBase