docstr: ivy.Container instance method variant of ivy.l1_loss. This method
  simply wraps the function, and so the docstring for ivy.l1_loss also
  applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      input container.
  target
      input array or container containing the targeticted values.
  reduction
      ``'mean'``: The output will be averaged.
      ``'sum'``: The output will be summed.
      ``'none'``: No reduction will be applied to the output. 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 L1 loss between the input array and the targeticted values.
  
  Examples
  --------
  >>> x = ivy.Container(a=ivy.array([1, 2, 3]), b=ivy.array([4, 5, 6]))
  >>> y = ivy.Container(a=ivy.array([2, 2, 2]), b=ivy.array([5, 5, 5]))
  >>> z = x.l1_loss(y)
  >>> print(z)
  {
      a: ivy.array(1.),
      b: ivy.array(0.)
  }
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code: def l1_loss(
      self: ivy.Container,
      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 instance method variant of ivy.l1_loss. This method
      simply wraps the function, and so the docstring for ivy.l1_loss also
      applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          input container.
      target
          input array or container containing the targeticted values.
      reduction
          ``'mean'``: The output will be averaged.
          ``'sum'``: The output will be summed.
          ``'none'``: No reduction will be applied to the output. 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 L1 loss between the input array and the targeticted values.
  
      Examples
      --------
      >>> x = ivy.Container(a=ivy.array([1, 2, 3]), b=ivy.array([4, 5, 6]))
      >>> y = ivy.Container(a=ivy.array([2, 2, 2]), b=ivy.array([5, 5, 5]))
      >>> z = x.l1_loss(y)
      >>> print(z)
      {
          a: ivy.array(1.),
          b: ivy.array(0.)
      }
      """
      return self._static_l1_loss(
          self,
          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 2 for calling the _ContainerWithLossesExperimental obj's underlying member method code with example usage: obj.l1_loss() and returns: self