docstr: ivy.Container instance method variant of ivy.smooth_l1_loss. This
  method simply wraps the function, and so the docstring for ivy.
  smooth_l1_loss also applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      input container containing input labels.
  target
      input array or container containing the targeticted labels.
  beta
      a positive float value that sets the smoothness threshold.
      Default: ``1.0``.
  reduction
      ``'none'``: No reduction will be applied to the output.
      ``'mean'``: The output will be averaged.
      ``'sum'``: The output will be summed. Default: ``'mean'``.
  key_chains
      The key-chains to apply or not apply the method to. Default is
      ``None``.
  to_apply
      If input, the method will be applied to key_chains, otherwise
      key_chains
      will be skipped. Default is ``input``.
  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 container, for writing the result to.
      It must have a shape
      that the inputs broadcast to.
  
  Returns
  -------
  ret
      The smooth L1 loss between the input array and the targeticted labels.
  
  Examples
  --------
  >>> x = ivy.Container(a=ivy.array([1, 0, 2]), b=ivy.array([3, 2, 1]))
  >>> y = ivy.Container(a=ivy.array([0.6, 0.2, 0.3]),
  b=ivy.array([0.8, 0.2, 0.2]))
  >>> z = x.smooth_l1_loss(y)
  >>> print(z)
  {
      a: ivy.array(0.9),
      b: ivy.array(0.25)
  }
docstr_tok: ["ivy.Container","instance","method","variant","of","ivy.smooth_l1_loss",".","This","method","simply","wraps","the","function",",","and","so","the","docstring","for","ivy",".","smooth_l1_loss","also","applies","to","this","method","with","minimal","changes",".","Parameters","--","--","--","--","--","self","input","container","containing","input","labels",".","target","input","array","or","container","containing","the","targeticted","labels",".","beta","a","positive","float","value","that","sets","the","smoothness","threshold",".","Default",":","``","1.0","``",".","reduction","``","'none","'","``",":","No","reduction","will","be","applied","to","the","output",".","``","'mean","'","``",":","The","output","will","be","averaged",".","``","'sum","'","``",":","The","output","will","be","summed",".","Default",":","``","'mean","'","``",".","key_chains","The","key-chains","to","apply","or","not","apply","the","method","to",".","Default","is","``","None","``",".","to_apply","If","input",",","the","method","will","be","applied","to","key_chains",",","otherwise","key_chains","will","be","skipped",".","Default","is","``","input","``",".","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","container",",","for","writing","the","result","to",".","It","must","have","a","shape","that","the","inputs","broadcast","to",".","Returns","--","--","--","-","ret","The","smooth","L1","loss","between","the","input","array","and","the","targeticted","labels",".","Examples","--","--","--","--",">",">",">","x","=","ivy.Container","(","a=ivy.array","(","[","1",",","0",",","2","]",")",",","b=ivy.array","(","[","3",",","2",",","1","]",")",")",">",">",">","y","=","ivy.Container","(","a=ivy.array","(","[","0.6",",","0.2",",","0.3","]",")",",","b=ivy.array","(","[","0.8",",","0.2",",","0.2","]",")",")",">",">",">","z","=","x.smooth_l1_loss","(","y",")",">",">",">","print","(","z",")","{","a",":","ivy.array","(","0.9",")",",","b",":","ivy.array","(","0.25",")","}"]
code: def smooth_l1_loss(
      self: ivy.Container,
      target: Union[ivy.Container, ivy.Array, ivy.NativeArray],
      /,
      *,
      beta: Optional[Union[float, ivy.Container]] = 1.0,
      reduction: Optional[Union[str, ivy.Container]] = "mean",
      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.smooth_l1_loss. This
      method simply wraps the function, and so the docstring for ivy.
      smooth_l1_loss also applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          input container containing input labels.
      target
          input array or container containing the targeticted labels.
      beta
          a positive float value that sets the smoothness threshold.
          Default: ``1.0``.
      reduction
          ``'none'``: No reduction will be applied to the output.
          ``'mean'``: The output will be averaged.
          ``'sum'``: The output will be summed. Default: ``'mean'``.
      key_chains
          The key-chains to apply or not apply the method to. Default is
          ``None``.
      to_apply
          If input, the method will be applied to key_chains, otherwise
          key_chains
          will be skipped. Default is ``input``.
      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 container, for writing the result to.
          It must have a shape
          that the inputs broadcast to.
  
      Returns
      -------
      ret
          The smooth L1 loss between the input array and the targeticted labels.
  
      Examples
      --------
      >>> x = ivy.Container(a=ivy.array([1, 0, 2]), b=ivy.array([3, 2, 1]))
      >>> y = ivy.Container(a=ivy.array([0.6, 0.2, 0.3]),
      b=ivy.array([0.8, 0.2, 0.2]))
      >>> z = x.smooth_l1_loss(y)
      >>> print(z)
      {
          a: ivy.array(0.9),
          b: ivy.array(0.25)
      }
      """
      return self._static_smooth_l1_loss(
          self,
          target,
          beta=beta,
          reduction=reduction,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          out=out,
      )
  
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 _ContainerWithLossesExperimental obj's underlying member method code with example usage: obj.smooth_l1_loss() and returns: self