code: def convert_NormalizeL2(
      func, opset_version, input_names, output_names, context
  ):
      if isinstance(func.axis, tuple) and len(func.axis) != 1:
          raise ValueError(
              "Normalization along with multiple axes ({}) are not supported in "
              "the ONNX's LpNormalization operator.".format(func.axis)
          )
      if abs(func.eps - 1e-5) > sys.float_info.epsilon:
          # default value of F.normaize eps is 1e-5
          raise ValueError(
              "'eps' is not supported in the ONNX's LpNormalization operator,"
              " so that ONNX-Chainer does not accept custom values for 'eps' "
              "({})".format(func.eps)
          )
  
      return (
          onnx_helper.make_node(
              "LpNormalization",
              input_names,
              output_names,
              axis=int(func.axis[0]),
              p=2,
          ),
      )
  
file_path: chainer/onnx_chainer/functions/normalization.py
imports: import sys
  import chainer
  import numpy
  from onnx_chainer.functions.array import get_slice_node
  from onnx_chainer.functions.opset_version import support
  from onnx_chainer import onnx_helper
label_desc: Use image node_id 5 for calling a global function with example usage: convert_NormalizeL2(func, opset_version, input_names, output_names, context) and returns: onnx_helper