code: def norm_normalization(x, p=None, axes=None, eps=1e-12):
      r"""
      Norm normalization.
  
      .. math::
          y = \frac{x_i}{\|x\|_p}
  
      Args:
          x(~nnabla.Variable): N-D array.
          p(float): Order of the norm.
              [default= `2` ]
          axes(repeated int64): Axes to be reduced. If empty list is given, all dimensions are reduced.
              [default= `range(x.ndim)` ]
          eps(float): Epsilon for the normalization. This `eps` is added before taking the p-th root in the norm computation.
              [default= `1e-12` ]
  
      Returns:
          ~nnabla.Variable: N-D array
      """
      from .function_bases import (
          norm_normalization as norm_normalization_base,
      )
  
      if axes is None:
          axes = range(x.ndim)
      elif not hasattr(axes, "__iter__"):
          axes = [axes]
      return norm_normalization_base(x, p, axes, eps)
  
imports: from __future__ import absolute_import
  from .function_bases import *
  from .None import dtypes
  import nnabla
  import numpy
  from .normalization_functions import *
  from .numpy_compat_functions import *
label_desc: Use image node_id 6 for calling a global function with example usage: norm_normalization(x, p, axes, eps) and returns: norm_normalization_base