code: def conv2d(
      g: jit_utils.GraphContext,
      input,
      weight,
      bias,
      stride,
      padding,
      dilation,
      groups,
      scale,
      zero_point,
  ):
      kernel_size = weight.node()["shape"][1:3]
      kwargs = {
          "strides_i": stride,
          "pads_i": padding + padding,
          "dilations_i": dilation,
          "group_i": groups,
          "kernels_i": kernel_size,
          "order_s": "NHWC",
          "Y_scale_f": scale,
          "Y_zero_point_i": zero_point,
      }
      output = g.op("_caffe2::Int8Conv", input, weight, bias, **kwargs)
      symbolic_helper._quantized_ops.add(output)
      return output
  
imports: import importlib
  import inspect
  from torch.onnx import symbolic_helper, symbolic_opset9
  from torch.onnx._internal import jit_utils, registration
label_desc: Use image node_id 8 for calling a global function with example usage: conv2d(g, input, weight, bias, stride, padding, dilation, groups, scale, zero_point) and returns: output