code: def convert_SplitAxis(
      func, opset_version, input_names, output_names, context
  ):
      if func.indices is not None:
          indices_or_sections = func.indices
      else:
          indices_or_sections = func.sections
  
      total = func.inputs[0].shape[func.axis]
      if hasattr(indices_or_sections, "__iter__"):
          split = []
          prev_i = 0
          for i in indices_or_sections:
              split.append(i - prev_i)
              prev_i = i
          split.append(total - prev_i)
      else:
          length = total // indices_or_sections
          split = [length for _ in range(indices_or_sections)]
  
      assert len(output_names) == len(split)
      if opset_version == 1:
          return (
              onnx_helper.make_node(
                  "Split",
                  input_names,
                  output_names,
                  axis=func.axis,
                  split=split,
              ),
          )
      elif opset_version == 2:
          return (
              onnx_helper.make_node(
                  "Split",
                  input_names,
                  output_names,
                  axis=func.axis,
                  split=split,
              ),
          )
  
imports: import warnings
  import chainer
  import numpy
  import onnx
  from onnx.mapping import NP_TYPE_TO_TENSOR_TYPE
  from onnx_chainer.functions.opset_version import support
  from onnx_chainer import onnx_helper
label_desc: Use image node_id 13 for calling a global function with example usage: convert_SplitAxis(func, opset_version, input_names, output_names, context) and returns: onnx_helper, onnx_helper