code: def convert_convolution(node, **kwargs):
      
      name, input_nodes, attrs = get_inputs(node, kwargs)
  
      kernel_dims = list(parse_helper(attrs, "kernel"))
      stride_dims = list(parse_helper(attrs, "stride", [1, 1]))
      pad_dims = list(parse_helper(attrs, "pad", [0, 0]))
      num_group = int(attrs.get("num_group", 1))
      dilations = list(parse_helper(attrs, "dilate", [1, 1]))
  
      pad_dims = pad_dims + pad_dims
  
      conv_node = onnx.helper.make_node(
          "Conv",
          inputs=input_nodes,
          outputs=[name],
          kernel_shape=kernel_dims,
          strides=stride_dims,
          dilations=dilations,
          pads=pad_dims,
          group=num_group,
          name=name
      )
  
      return [conv_node]
docs: Map MXNet's convolution operator attributes to onnx's Conv operator
      and return the created node.