code: def quantize_and_calibrate(net, dataloader):
      class QuantizationDataLoader(mx.gluon.data.DataLoader):
          def __init__(self, dataloader, use_segmentation):
              self._dataloader = dataloader
              self._iter = None
              self._use_segmentation = use_segmentation
  
          def __iter__(self):
              self._iter = iter(self._dataloader)
              return self
  
          def __next__(self):
              batch = next(self._iter)
              if self._use_segmentation:
                  return [
                      batch.data,
                      batch.segment_ids,
                      batch.valid_length,
                  ]
              else:
                  return [batch.data, batch.valid_length]
  
          def __del__(self):
              del self._dataloader
  
      class BertLayerCollector(
          mx.contrib.quantization.CalibrationCollector
      ):
          """Saves layer output min and max values in a dict with layer names as keys.
          The collected min and max values will be directly used as thresholds for quantization.
          """
  
          def __init__(self, clip_min, clip_max):
              super(BertLayerCollector, self).__init__()
              self.clip_min = clip_min
              self.clip_max = clip_max
  
          def collect(self, name, op_name, arr):
              """Callback function for collecting min and max values from an NDArray."""
              if name not in self.include_layers:
                  return
              arr = arr.copyto(mx.cpu()).asnumpy()
              min_range = np.min(arr)
              max_range = np.max(arr)
  
              if (
                  name.find("sg_onednn_fully_connected_eltwise") != -1
                  or op_name.find("LayerNorm") != -1
              ) and max_range > self.clip_max:
                  max_range = self.clip_max
              elif (
                  name.find("sg_onednn_fully_connected") != -1
                  and min_range < self.clip_min
              ):
                  min_range = self.clip_min
  
              if name in self.min_max_dict:
                  cur_min_max = self.min_max_dict[name]
                  self.min_max_dict[name] = (
                      min(cur_min_max[0], min_range),
                      max(cur_min_max[1], max_range),
                  )
              else:
                  self.min_max_dict[name] = (min_range, max_range)
  
      calib_data = QuantizationDataLoader(
          dataloader, net.use_segmentation
      )
      model_name = args.model_name
      # disable specific layers in some models for the sake of accuracy
  
      if model_name == "google_albert_base_v2":
          logging.warn(
              f"Currently quantized {model_name} shows significant accuracy drop which is not fixed yet"
          )
  
      exclude_layers_map = {
          "google_electra_large": [
              "sg_onednn_fully_connected_eltwise_2",
              "sg_onednn_fully_connected_eltwise_14",
              "sg_onednn_fully_connected_eltwise_18",
              "sg_onednn_fully_connected_eltwise_22",
              "sg_onednn_fully_connected_eltwise_26",
          ]
      }
      exclude_layers = None
      if model_name in exclude_layers_map.keys():
          exclude_layers = exclude_layers_map[model_name]
      net.quantized_backbone = mx.contrib.quant.quantize_net(
          net.backbone,
          quantized_dtype="auto",
          quantize_mode="smart",
          exclude_layers=exclude_layers,
          exclude_layers_match=None,
          calib_data=calib_data,
          calib_mode="custom",
          LayerOutputCollector=BertLayerCollector(
              clip_min=-50, clip_max=10
          ),
          num_calib_batches=10,
          ctx=mx.cpu(),
      )
      return net
  
imports: import os
  import json
  import time
  import logging
  import argparse
  import ast
  import functools
  import collections
  import dataclasses
  from dataclasses import dataclass
  from multiprocessing import Pool, cpu_count
  import mxnet
  import numpy
  from mxnet.lr_scheduler import PolyScheduler
  import gluonnlp.data.batchify
  from models import ModelForQABasic, ModelForQAConditionalV1
  from eval_utils import squad_eval
  from squad_utils import SquadFeature, get_squad_examples, convert_squad_example_to_feature
  from gluonnlp.models import get_backbone
  from gluonnlp.utils.misc import repeat, grouper, set_seed, init_comm, logging_config, parse_ctx
  from gluonnlp.initializer import TruncNorm
  from gluonnlp.data.sampler import SplitSampler
  from gluonnlp.utils.parameter import grad_global_norm, clip_grad_global_norm, count_parameters, deduplicate_param_dict
label_desc: Use image node_id 7 for calling a global function with example usage: quantize_and_calibrate(net, dataloader) and returns: net, self, list, list