code: def convert_DeconvolutionND(
      func, opset_version, input_names, output_names, context
  ):
      pad = []
      x_ndim = len(func.inputs[0].shape)
      w_ndim = len(func.inputs[1].shape)
      for _ in range(x_ndim - w_ndim):
          pad.append(0)
      for p in func.pad:
          pad.append(p)
      pad = pad * 2
  
      return (
          onnx_helper.make_node(
              "ConvTranspose",
              input_names,
              output_names,
              kernel_shape=func.inputs[1].shape[2:],
              output_shape=func.outs,
              pads=pad,
              strides=func.stride,
              group=func.groups,
          ),
      )
  
file_path: chainer/onnx_chainer/functions/connection.py
imports: import numpy
  from onnx_chainer.functions.opset_version import support
  from onnx_chainer import onnx_helper
label_desc: Use image node_id 4 for calling a global function with example usage: convert_DeconvolutionND(func, opset_version, input_names, output_names, context) and returns: onnx_helper