docstr: ivy.Array 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
      input array containing true labels.
  target
      input array containing targeted labels.
  reduction : str, optional
      The type of reduction to apply to the loss.
      Possible values are "mean" (default)
      and "sum".
  delta
      The threshold parameter that determines the point where the loss transitions
      from squared error to absolute error. Default is 1.0.
  out
      Optional output array, for writing the result to. It must have a shape
      that the inputs broadcast to.
  
  Returns
  -------
  ret
      The Huber loss between the true and predicted values.
  
  Examples
  --------
  >>> true = ivy.array([2, 4, 7, 1])
  >>> pred = ivy.array([2.5, 3.5, 8, 0.8])
  >>> loss = true.huber_loss(pred, delta=1.0)
  >>> print(loss)
  ivy.array([0.125, 0.125, 0.5  , 0.125])
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code: def huber_loss(
      self: ivy.Array,
      target: Union[ivy.Array, ivy.NativeArray],
      /,
      *,
      reduction: Optional[str] = "mean",
      delta: Optional[float] = 1.0,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """ivy.Array 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
          input array containing true labels.
      target
          input array containing targeted labels.
      reduction : str, optional
          The type of reduction to apply to the loss.
          Possible values are "mean" (default)
          and "sum".
      delta
          The threshold parameter that determines the point where the loss transitions
          from squared error to absolute error. Default is 1.0.
      out
          Optional output array, for writing the result to. It must have a shape
          that the inputs broadcast to.
  
      Returns
      -------
      ret
          The Huber loss between the true and predicted values.
  
      Examples
      --------
      >>> true = ivy.array([2, 4, 7, 1])
      >>> pred = ivy.array([2.5, 3.5, 8, 0.8])
      >>> loss = true.huber_loss(pred, delta=1.0)
      >>> print(loss)
      ivy.array([0.125, 0.125, 0.5  , 0.125])
      """
      return ivy.huber_loss(
          self._data, target, reduction=reduction, delta=delta, out=out
      )
  
file_path: ivy/ivy/data_classes/array/experimental/losses.py
imports: import abc
  from typing import Optional, Union
  import ivy
label_desc: Use image node_id 3 for calling the _ArrayWithLossesExperimental obj's underlying member method code with example usage: obj.huber_loss() and returns: ivy