docstr: ivy.Container static method variant of ivy.dropout1d. This method
  simply wraps the function, and so the docstring for ivy.dropout1d also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  x
      The input container to perform dropout on.
  prob
      The probability of zeroing out each array element, float between 0 and 1.
  training
      Turn on dropout if training, turn off otherwise. Default is ``True``.
  data_format
      "NWC" or "NCW". Default is ``"NCW"``.
  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 container of the output after dropout is performed.
  
  Examples
  --------
  >>> x = ivy.Container(a=ivy.array([1, 2, 3]).reshape([1, 1, 3]),
  ...                   b=ivy.array([4, 5, 6]).reshape([1, 1, 3]))
  >>> y = ivy.Container.static_dropout1d(x, 0.5)
  >>> print(y)
  {
      a: ivy.array([[[0., 4., 0.]]]),
      b: ivy.array([[[0., 0., 12.]]])
  }
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code: def _static_dropout1d(
      x: ivy.Container,
      prob: Union[float, ivy.Container],
      /,
      *,
      training: Union[bool, ivy.Container] = True,
      data_format: Union[str, ivy.Container] = "NWC",
      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 static method variant of ivy.dropout1d. This method
      simply wraps the function, and so the docstring for ivy.dropout1d also
      applies to this method with minimal changes.
  
      Parameters
      ----------
      x
          The input container to perform dropout on.
      prob
          The probability of zeroing out each array element, float between 0 and 1.
      training
          Turn on dropout if training, turn off otherwise. Default is ``True``.
      data_format
          "NWC" or "NCW". Default is ``"NCW"``.
      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 container of the output after dropout is performed.
  
      Examples
      --------
      >>> x = ivy.Container(a=ivy.array([1, 2, 3]).reshape([1, 1, 3]),
      ...                   b=ivy.array([4, 5, 6]).reshape([1, 1, 3]))
      >>> y = ivy.Container.static_dropout1d(x, 0.5)
      >>> print(y)
      {
          a: ivy.array([[[0., 4., 0.]]]),
          b: ivy.array([[[0., 0., 12.]]])
      }
      """
      return ContainerBase.cont_multi_map_in_function(
          "dropout1d",
          x,
          prob,
          training=training,
          data_format=data_format,
          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 5 for calling the _ContainerWithLayers obj's underlying member method code with example usage: obj._static_dropout1d() and returns: ContainerBase