code: def convert_Convolution2DFunction(
      func, opset_version, input_names, output_names, context
  ):
      if hasattr(func, "dy") and hasattr(func, "dx"):
          node = onnx_helper.make_node(
              "Conv",
              input_names,
              output_names,
              dilations=(func.dy, func.dx),
              kernel_shape=func.inputs[1].shape[2:],
              # pads: [x1_begin, x2_begin...x1_end, x2_end,...]
              pads=(func.ph, func.pw, func.ph, func.pw),
              strides=(func.sy, func.sx),
              group=func.groups,
          )
      else:
          node = onnx_helper.make_node(
              "Conv",
              input_names,
              output_names,
              dilations=(1, 1),
              kernel_shape=func.inputs[1].shape[2:],
              pads=(func.ph, func.pw, func.ph, func.pw),
              strides=(func.sy, func.sx),
              group=func.groups,
          )
      return (node,)
  
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 1 for calling a global function with example usage: convert_Convolution2DFunction(func, opset_version, input_names, output_names, context) and returns: node