docstr: ivy.Container static method variant of ivy.soft_margin_loss. This
  method simply wraps the function, and so the docstring for
  ivy.soft_margin_loss also applies to this method with minimal changes.
  
  # Insert the docstring here
  
  Parameters
  ----------
  input
      input array or container containing input labels.
  target
      input array or container containing the targeticted labels.
  reduction
      the reduction method. 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 soft margin loss between the given distributions.
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code: def _static_soft_margin_loss(
      input: Union[ivy.Container, ivy.Array, ivy.NativeArray],
      target: Union[ivy.Container, ivy.Array, ivy.NativeArray],
      /,
      *,
      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 static method variant of ivy.soft_margin_loss. This
      method simply wraps the function, and so the docstring for
      ivy.soft_margin_loss also applies to this method with minimal changes.
  
      # Insert the docstring here
  
      Parameters
      ----------
      input
          input array or container containing input labels.
      target
          input array or container containing the targeticted labels.
      reduction
          the reduction method. 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 soft margin loss between the given distributions.
      """
      return ContainerBase.cont_multi_map_in_function(
          "soft_margin_loss",
          input,
          target,
          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 9 for calling the _ContainerWithLossesExperimental obj's underlying member method code with example usage: obj._static_soft_margin_loss() and returns: ContainerBase