docstr: ivy.Container static method variant of ivy.lp_normalize. This method
  simply wraps the function, and so the docstring for ivy.lp_normalize
  also applies to this method with minimal changes.
  
  Parameters
  ----------
  x
      The input container with leaves to be normalized.
  p
      The order of the norm.
  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.
  
  Examples
  --------
  >>> x = ivy.Container(a=ivy.array([[0.5, 1.5, 2.5], [3.5, 4.5, 5.5]])))
  ...                    b=ivy.array([[-1., -1.], [-1., -0.5]]]))
  >>> y = ivy.Container.static_lp_normalize(x, p=1, axis=1)
  >>> print(y)
  {
      a: ivy.array([[0.12500000, 0.37500000, 0.62500000],
                    [0.27500000, 0.35000000, 0.42500000]]),
      b: ivy.array([[-1.0000000, -1.0000000],
                    [-0.5000000, -0.2500000]])
  }
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code: def static_lp_normalize(
      x: Union[ivy.Container, ivy.Array, ivy.NativeArray],
      p: Union[float, ivy.Container] = 2,
      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 static method variant of ivy.lp_normalize. This method
      simply wraps the function, and so the docstring for ivy.lp_normalize
      also applies to this method with minimal changes.
  
      Parameters
      ----------
      x
          The input container with leaves to be normalized.
      p
          The order of the norm.
      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.
  
      Examples
      --------
      >>> x = ivy.Container(a=ivy.array([[0.5, 1.5, 2.5], [3.5, 4.5, 5.5]])))
      ...                    b=ivy.array([[-1., -1.], [-1., -0.5]]]))
      >>> y = ivy.Container.static_lp_normalize(x, p=1, axis=1)
      >>> print(y)
      {
          a: ivy.array([[0.12500000, 0.37500000, 0.62500000],
                        [0.27500000, 0.35000000, 0.42500000]]),
          b: ivy.array([[-1.0000000, -1.0000000],
                        [-0.5000000, -0.2500000]])
      }
      """
      return ContainerBase.cont_multi_map_in_function(
          "lp_normalize",
          x,
          p=p,
          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 11 for calling the _ContainerWithNormsExperimental obj's underlying member method code with example usage: obj.static_lp_normalize(x, p, axis, key_chains, to_apply, prune_unapplied, map_sequences, out) and returns: ContainerBase