docstr: ivy.Container static method variant of ivy.hinge_embedding_loss.
  This method simplywraps the function, and so the docstring for
  ivy.hinge_embedding_loss also applies to this method with minimal
  changes.
  
  Parameters
  ----------
  input
      input array or container containing input labels.
  target
      input array or container containing the target labels.
  margin
      Sets the hyperparameter margin. Determines the necessary input size
      for hinge_embedding_loss calculations when label is -1. Inputs smaller
      than the margin are minimized with hinge_embedding_loss.
      Default is 1.0.
  reduction
      Specifies how to aggregate the loss across the batch. Options are:
      - ``'none'``: Returns the unreduced loss.
      - ``'mean'``: Returns the mean loss.
      - ``'sum'``: Returns the summed loss.
      Default is ``'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``.
  
  Shape
  -----
      - Input: :math:`(*)` where :math:`*` means, any number of dimensions. \
      The sum operation operates over all the elements.
      - Target: :math:`(*)`, same shape as the input
      - Output: scalar. If :attr:`reduction` is ``'none'``,
      then same shape as the input
  
  Returns
  -------
  ret
      Hinge embedding loss calculated from the input and label,
      shaped based on the reduction method.
  
  Examples
  --------
  With :class:`ivy.Container` inputs:
  
  >>> x = ivy.Container(a=ivy.array([[1, 0, 2]], dtype=ivy.float32),
  ...             b=ivy.array([[-1, 1, 1]], dtype=ivy.float32))
  >>> y = ivy.Container(a=ivy.array([[0.6, 0.2, 0.3]], dtype=ivy.float32),
  ...            b=ivy.array([[1, 1, 1]], dtype=ivy.float32))
  >>> z = ivy.Container._static_hinge_embedding_loss(x, y, reduction="none")
  >>> z
  {
      a: ivy.array([[0., 0., 0.]]),
      b: ivy.array([[-1., 1., 1.]])
  }
  
  With a mix of :class:`ivy.Array` and :class:`ivy.Container` inputs:
  
  >>> x = ivy.array([[10, 20, 32]], dtype=ivy.float32)
  >>> y = ivy.Container(a=ivy.array([[-1, -1, -1]], dtype=ivy.float32),
  ...           b=ivy.array([[1, 1, 1]], dtype=ivy.float32))
  >>> z = ivy.Container._static_hinge_embedding_loss(x, y,
  ...                             reduction="sum", margin=2.0)
  >>> z
  {
      a: ivy.array(0.),
      b: ivy.array(62.)
  }
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code: def _static_hinge_embedding_loss(
      input: Union[ivy.Container, ivy.Array, ivy.NativeArray],
      target: Union[ivy.Container, ivy.Array, ivy.NativeArray],
      *,
      margin: [Union[float, ivy.Container]] = 1.0,
      reduction: [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,
  ) -> ivy.Container:
      r"""ivy.Container static method variant of ivy.hinge_embedding_loss.
          This method simplywraps the function, and so the docstring for
          ivy.hinge_embedding_loss also applies to this method with minimal
          changes.
  
          Parameters
          ----------
          input
              input array or container containing input labels.
          target
              input array or container containing the target labels.
          margin
              Sets the hyperparameter margin. Determines the necessary input size
              for hinge_embedding_loss calculations when label is -1. Inputs smaller
              than the margin are minimized with hinge_embedding_loss.
              Default is 1.0.
          reduction
              Specifies how to aggregate the loss across the batch. Options are:
              - ``'none'``: Returns the unreduced loss.
              - ``'mean'``: Returns the mean loss.
              - ``'sum'``: Returns the summed loss.
              Default is ``'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``.
  
          Shape
          -----
              - Input: :math:`(*)` where :math:`*` means, any number of dimensions. \
              The sum operation operates over all the elements.
              - Target: :math:`(*)`, same shape as the input
              - Output: scalar. If :attr:`reduction` is ``'none'``,
              then same shape as the input
  
          Returns
          -------
          ret
              Hinge embedding loss calculated from the input and label,
              shaped based on the reduction method.
  
          Examples
          --------
          With :class:`ivy.Container` inputs:
  
          >>> x = ivy.Container(a=ivy.array([[1, 0, 2]], dtype=ivy.float32),
          ...             b=ivy.array([[-1, 1, 1]], dtype=ivy.float32))
          >>> y = ivy.Container(a=ivy.array([[0.6, 0.2, 0.3]], dtype=ivy.float32),
          ...            b=ivy.array([[1, 1, 1]], dtype=ivy.float32))
          >>> z = ivy.Container._static_hinge_embedding_loss(x, y, reduction="none")
          >>> z
          {
              a: ivy.array([[0., 0., 0.]]),
              b: ivy.array([[-1., 1., 1.]])
          }
  
          With a mix of :class:`ivy.Array` and :class:`ivy.Container` inputs:
  
          >>> x = ivy.array([[10, 20, 32]], dtype=ivy.float32)
          >>> y = ivy.Container(a=ivy.array([[-1, -1, -1]], dtype=ivy.float32),
          ...           b=ivy.array([[1, 1, 1]], dtype=ivy.float32))
          >>> z = ivy.Container._static_hinge_embedding_loss(x, y,
          ...                             reduction="sum", margin=2.0)
          >>> z
          {
              a: ivy.array(0.),
              b: ivy.array(62.)
          }
          """
      return ContainerBase.cont_multi_map_in_function(
          "hinge_embedding_loss",
          input,
          target,
          margin=margin,
          reduction=reduction,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
      )
  
imports: from typing import Optional, Union, List, Dict
  import ivy
  from ivy.data_classes.container.base import ContainerBase
label_desc: Use image node_id 15 for calling the _ContainerWithLossesExperimental obj's underlying member method code with example usage: obj._static_hinge_embedding_loss(input, target) and returns: ContainerBase