code: def calib_graph(
      qsym,
      arg_params,
      aux_params,
      collector,
      calib_mode="entropy",
      logger=None,
  ):
      """User-level API for calibrating a quantized model using a filled collector.
      The backend quantized operators are only enabled for Linux systems. Please do not run
      inference using the quantized models on Windows for now.
  
      Parameters
      ----------
      qsym : Symbol
          Defines the structure of a neural network for INT8 data types.
      arg_params : dict
          Dictionary of name to `NDArray`.
      aux_params : dict
          Dictionary of name to `NDArray`.
      collector : function
          layer collector for naive or entropy calibration.
      calib_mode : str
          If calib_mode='none', no calibration will be used and the thresholds for
          requantization after the corresponding layers will be calculated at runtime by
          calling min and max operators. The quantized models generated in this
          mode are normally 10-20% slower than those with calibrations during inference.
          If calib_mode='naive', the min and max values of the layer outputs from a calibration
          dataset will be directly taken as the thresholds for quantization.
          If calib_mode='entropy' (default mode), the thresholds for quantization will be
          derived such that the KL divergence between the distributions of FP32 layer outputs and
          quantized layer outputs is minimized based upon the calibration dataset.
      quantized_dtype : str
          The quantized destination type for input data. Currently support 'int8'
          , 'uint8' and 'auto'. 'auto' means automatically select output type according to calibration result.
          Default value is 'int8'.
      logger : Object
          A logging object for printing information during the process of quantization.
      Returns
      -------
      quantized_model : tuple
          A tuple of calibrated symbol, quantized arg_params, aux_params.
      """
      min_max_dict = {}
      if calib_mode is not None and calib_mode != "none":
          if calib_mode in ("entropy", "naive", "custom"):
              min_max_dict = collector.post_collect()
  
          else:
              raise ValueError(
                  f"unknown calibration mode {calib_mode} received,"
                  " expected `none`, `naive`, `entropy` or `custom`"
              )
          qsym = _calibrate_quantized_sym(qsym, min_max_dict)
      else:
          raise ValueError(
              "Please set calibration mode to naive, entropy or custom (with custom CalibrationCollector)"
          )
  
      if logger:
          logger.info("Quantizing parameters")
      qarg_params = _quantize_params(qsym, arg_params, min_max_dict)
  
      if is_np_array():
          qsym = qsym.as_np_ndarray()
  
      return qsym, qarg_params, aux_params
  
imports: import abc
  import ctypes
  import logging
  import os
  import warnings
  import numpy
  import mxnet
  from ..base import _LIB, check_call, py_str
  from ..base import c_array, c_str, mx_uint, mx_real_t, c_str_array
  from ..base import SymbolHandle
  from ..symbol import Symbol
  from ..None import ndarray
  from ..io import DataDesc
  from ..device import cpu, Device
  from ..util import is_np_array, wrap_ctx_to_device_func
label_desc: Use image node_id 10 for calling a global function with example usage: calib_graph(qsym, arg_params, aux_params, collector, calib_mode, logger) and returns: qsym,  qarg_params,  aux_params