docstr: ivy.Container instance method variant of huber_loss. This method
  simply wraps the function, and so the docstring for huber_loss also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      true container containing true labels.
  pred
      true array or container containing the predicted labels.
  delta
      The threshold parameter that determines the point where the loss transitions
      from squared error to absolute error. Default is 1.0.
  reduction : str, optional
      The type of reduction to apply to the loss.
      Possible values are "mean" (default)
      and "sum".
  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 container, for writing the result to. It must have a shape
      that the trues broadcast to.
  
  Returns
  -------
  ret
      The Huber loss between the true and predicted values.
  
  Examples
  --------
  >>> x = ivy.Container(a=ivy.array([1, 0, 3]), b=ivy.array([0, 0, 2]))
  >>> y = ivy.Container(a=ivy.array([1.5, 0.2, 2.8]), b=ivy.array([0.5, 0.2, 1.9])
  )
  >>> z = x.huber_loss(y, delta=1.0)
  >>> print(z)
  {
      a: ivy.array(0.0575),
      b: ivy.array(0.005)
  }
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code: def huber_loss(
      self: ivy.Container,
      pred: Union[ivy.Container, ivy.Array, ivy.NativeArray],
      /,
      *,
      delta: 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 huber_loss. This method
      simply wraps the function, and so the docstring for huber_loss also
      applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          true container containing true labels.
      pred
          true array or container containing the predicted labels.
      delta
          The threshold parameter that determines the point where the loss transitions
          from squared error to absolute error. Default is 1.0.
      reduction : str, optional
          The type of reduction to apply to the loss.
          Possible values are "mean" (default)
          and "sum".
      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 container, for writing the result to. It must have a shape
          that the trues broadcast to.
  
      Returns
      -------
      ret
          The Huber loss between the true and predicted values.
  
      Examples
      --------
      >>> x = ivy.Container(a=ivy.array([1, 0, 3]), b=ivy.array([0, 0, 2]))
      >>> y = ivy.Container(a=ivy.array([1.5, 0.2, 2.8]), b=ivy.array([0.5, 0.2, 1.9])
      )
      >>> z = x.huber_loss(y, delta=1.0)
      >>> print(z)
      {
          a: ivy.array(0.0575),
          b: ivy.array(0.005)
      }
      """
      return self._static_huber_loss(
          self,
          pred,
          delta=delta,
          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 8 for calling the _ContainerWithLossesExperimental obj's underlying member method code with example usage: obj.huber_loss() and returns: self