docstr: ivy.Container instance method variant of ivy.l1_normalize. This
  method simply wraps the function, and so the docstring for
  ivy.l1_normalize also applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      The input container with leaves to be normalized.
  axis
      The axis along which to normalize.
  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 inputs broadcast to.
  
  Returns
  -------
  ret
      A container containing the normalized leaves.
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code: def l1_normalize(
      self: ivy.Container,
      axis: Optional[Union[int, ivy.Container]] = None,
      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_normalize. This
      method simply wraps the function, and so the docstring for
      ivy.l1_normalize also applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          The input container with leaves to be normalized.
      axis
          The axis along which to normalize.
      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 inputs broadcast to.
  
      Returns
      -------
      ret
          A container containing the normalized leaves.
      """
      return self.static_l1_normalize(
          self,
          axis=axis,
          key_chains=key_chains,
          to_apply=to_apply,
          prune_unapplied=prune_unapplied,
          map_sequences=map_sequences,
          out=out,
      )
  
file_path: ivy/ivy/data_classes/container/experimental/norms.py
imports: from ivy.data_classes.container.base import ContainerBase
  from typing import Union, List, Dict, Optional, Tuple
  import ivy
label_desc: Use image node_id 2 for calling the _ContainerWithNormsExperimental obj's underlying member method code with example usage: obj.l1_normalize(axis, key_chains, to_apply, prune_unapplied, map_sequences, out) and returns: self