docstr: ivy.Container instance method variant of ivy.dropout. This method
  simply wraps the function, and so the docstring for ivy.dropout also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      The input container to perform dropout on.
  prob
      The probability of zeroing out each array element, float between 0 and 1.
  scale
      Whether to scale the output by `1/(1-prob)`, default is ``True``.
  dtype
      output array data type. If dtype is None, the output array data type
      must be inferred from x. Default: ``None``.
  training
      Turn on dropout if training, turn off otherwise. Default is ``True``.
  seed
      Set a default seed for random number generating (for reproducibility).
      Default is ``None``.
  noise_shape
      a sequence representing the shape of the binary dropout mask that will be
      multiplied with the input.
  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``.
  out
      optional output array, for writing the result to. It must have a shape
      that the inputs broadcast to.
  
  Returns
  -------
  ret
      Result array of the output after dropout is performed.
  
  Examples
  --------
  >>> x = ivy.Container(a=ivy.array([[1., 2., 3.], [4., 5., 6.]]),
  ...                   b=ivy.array([7., 8., 9.]))
  >>> y = x.dropout(0.3)
  >>> print(y)
  {
      a: ivy.array([[0., 0., 4.28571415],
                    [5.71428585, 7.14285755, 0.]]),
      b: ivy.array([0., 11.4285717, 12.8571434])
  }
docstr_tok: ["ivy.Container","instance","method","variant","of","ivy.dropout",".","This","method","simply","wraps","the","function",",","and","so","the","docstring","for","ivy.dropout","also","applies","to","this","method","with","minimal","changes",".","Parameters","--","--","--","--","--","self","The","input","container","to","perform","dropout","on",".","prob","The","probability","of","zeroing","out","each","array","element",",","float","between","0","and","1.","scale","Whether","to","scale","the","output","by","`","1\/","(","1-prob",")","`",",","default","is","``","True","``",".","dtype","output","array","data","type",".","If","dtype","is","None",",","the","output","array","data","type","must","be","inferred","from","x",".","Default",":","``","None","``",".","training","Turn","on","dropout","if","training",",","turn","off","otherwise",".","Default","is","``","True","``",".","seed","Set","a","default","seed","for","random","number","generating","(","for","reproducibility",")",".","Default","is","``","None","``",".","noise_shape","a","sequence","representing","the","shape","of","the","binary","dropout","mask","that","will","be","multiplied","with","the","input",".","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","``",".","out","optional","output","array",",","for","writing","the","result","to",".","It","must","have","a","shape","that","the","inputs","broadcast","to",".","Returns","--","--","--","-","ret","Result","array","of","the","output","after","dropout","is","performed",".","Examples","--","--","--","--",">",">",">","x","=","ivy.Container","(","a=ivy.array","(","[","[","1.",",","2.",",","3",".","]",",","[","4.",",","5.",",","6",".","]","]",")",",","...","b=ivy.array","(","[","7.",",","8.",",","9",".","]",")",")",">",">",">","y","=","x.dropout","(","0.3",")",">",">",">","print","(","y",")","{","a",":","ivy.array","(","[","[","0.",",","0.",",","4.28571415","]",",","[","5.71428585",",","7.14285755",",","0",".","]","]",")",",","b",":","ivy.array","(","[","0.",",","11.4285717",",","12.8571434","]",")","}"]
code: def dropout(
      self: ivy.Container,
      prob: Union[float, ivy.Container],
      /,
      *,
      scale: Union[bool, ivy.Container] = True,
      dtype: Optional[Union[ivy.Dtype, ivy.Container]] = None,
      training: Union[bool, ivy.Container] = True,
      seed: Optional[Union[int, ivy.Container]] = None,
      noise_shape: Optional[Union[Sequence[int], 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,
      out: Optional[ivy.Container] = None,
  ) -> ivy.Container:
      """ivy.Container instance method variant of ivy.dropout. This method
      simply wraps the function, and so the docstring for ivy.dropout also
      applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          The input container to perform dropout on.
      prob
          The probability of zeroing out each array element, float between 0 and 1.
      scale
          Whether to scale the output by `1/(1-prob)`, default is ``True``.
      dtype
          output array data type. If dtype is None, the output array data type
          must be inferred from x. Default: ``None``.
      training
          Turn on dropout if training, turn off otherwise. Default is ``True``.
      seed
          Set a default seed for random number generating (for reproducibility).
          Default is ``None``.
      noise_shape
          a sequence representing the shape of the binary dropout mask that will be
          multiplied with the input.
      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``.
      out
          optional output array, for writing the result to. It must have a shape
          that the inputs broadcast to.
  
      Returns
      -------
      ret
          Result array of the output after dropout is performed.
  
      Examples
      --------
      >>> x = ivy.Container(a=ivy.array([[1., 2., 3.], [4., 5., 6.]]),
      ...                   b=ivy.array([7., 8., 9.]))
      >>> y = x.dropout(0.3)
      >>> print(y)
      {
          a: ivy.array([[0., 0., 4.28571415],
                        [5.71428585, 7.14285755, 0.]]),
          b: ivy.array([0., 11.4285717, 12.8571434])
      }
      """
      return self._static_dropout(
          self,
          prob,
          scale=scale,
          dtype=dtype,
          training=training,
          seed=seed,
          noise_shape=noise_shape,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          out=out,
      )
  
imports: from typing import Optional, Tuple, Union, List, Callable, Dict, Sequence
  from ivy.data_classes.container.base import ContainerBase
  import ivy
label_desc: Use image node_id 4 for calling the _ContainerWithLayers obj's underlying member method code with example usage: obj.dropout() and returns: self