code: def quantized_cat(
      g: jit_utils.GraphContext,
      q_inputs: _C.Value,
      dim: int,
      op_scale: _C.Value,
      op_zero_point: _C.Value,
  ) -> _C.Value:
      unpacked_inputs = symbolic_helper._unpack_list(q_inputs)
      dequantized = [
          symbolic_helper.dequantize_helper(g, input)[0]
          for input in unpacked_inputs
      ]
      concatenated = g.op("Concat", *dequantized, axis_i=dim)
      return symbolic_helper.quantize_helper(
          g, concatenated, op_scale, op_zero_point
      )
  
imports: from __future__ import annotations
  import functools
  import sys
  import warnings
  from typing import List, Optional, Sequence, Tuple, Union
  import torch
  import torch._C._onnx
  import torch.onnx
  from torch import _C
  from torch.onnx import _constants, _type_utils, errors, symbolic_helper, symbolic_opset9
  from torch.onnx._globals import GLOBALS
  from torch.onnx._internal import _beartype, jit_utils, registration
label_desc: Use image node_id 46 for calling a global function with example usage: quantized_cat(g, q_inputs, dim, op_scale, op_zero_point) and returns: symbolic_helper