code: def evaluate(args, last=True):
      ctx_l = parse_ctx(args.gpus)
      setup_logging(args, 0)
  
      cfg, tokenizer, qa_net, use_segmentation = get_network(
          args.model_name,
          ctx_l,
          args.classifier_dropout,
          dtype="float32",
      )
  
      dev_data_path = os.path.join(
          args.data_dir, "dev-v{}.json".format(args.version)
      )
      with open(dev_data_path, "r") as f:
          dev_dataset = json.load(f)["data"]
      expl = get_squad_examples_from_json(
          dev_data_path, is_training=False
      )
      import random
  
      random.shuffle(expl)
      expl = expl[: 512 + 256]
      eval_qa_ids = {qa.qas_id for qa in expl}
      for article in dev_dataset:
          for paragraph in article["paragraphs"]:
              new_qas = []
              for qa in paragraph["qas"]:
                  if qa["id"] in eval_qa_ids:
                      new_qas.append(qa)
              paragraph["qas"] = new_qas
  
      num_process = min(cpu_count(), 8)
      with Pool(num_process) as pool:
          dev_features = pool.map(
              functools.partial(
                  convert_squad_example_to_feature,
                  tokenizer=tokenizer,
                  is_training=False,
              ),
              expl,
          )
      dataset_processor = SquadDatasetProcessor(
          tokenizer=tokenizer,
          doc_stride=args.doc_stride,
          max_seq_length=args.max_seq_length,
          max_query_length=args.max_query_length,
      )
      dev_all_chunk_features = []
      dev_chunk_feature_ptr = [0]
      for feature in dev_features:
          chunk_features = dataset_processor.process_sample(feature)
          dev_all_chunk_features.extend(chunk_features)
          dev_chunk_feature_ptr.append(
              dev_chunk_feature_ptr[-1] + len(chunk_features)
          )
  
      class QuantizationDataLoader(mx.gluon.data.DataLoader):
          def __init__(self, dataloader, use_segmentation):
              self._dataloader = dataloader
              self._iter = None
              self._use_segmentation = use_segmentation
              self.batch_size = args.eval_batch_size
              self._batch_sampler = dataloader._batch_sampler
  
          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
  
      dev_dataloader = QuantizationDataLoader(
          mx.gluon.data.DataLoader(
              dev_all_chunk_features,
              batchify_fn=dataset_processor.BatchifyFunction,
              batch_size=args.eval_batch_size,
              num_workers=0,
              shuffle=False,
          ),
          qa_net.use_segmentation,
      )
  
      def quantize_and_calibrate(net):
          from neural_compressor.experimental import (
              Quantization,
              common,
          )
  
          quantizer = Quantization("albert_custom.yaml")
          quantizer.model = common.Model(net.backbone)
          quantizer.calib_dataloader = dev_dataloader
          quantizer.eval_func = eval_validation
          net.quantized_backbone = quantizer().model
          net.quantized_backbone.export("best_conf_inc_model")
          return net
  
      def eval_validation(backbone):
          """
          Model inference during validation or final evaluation.
          """
          del qa_net.quantized_backbone
          qa_net.quantized_backbone = backbone
  
          dev_dataloader = mx.gluon.data.DataLoader(
              dev_all_chunk_features,
              batchify_fn=dataset_processor.BatchifyFunction,
              batch_size=args.eval_batch_size,
              num_workers=0,
              shuffle=False,
          )
  
          log_interval = args.eval_log_interval
          all_results = []
          epoch_tic = time.time()
          tic = time.time()
          epoch_size = len(dev_features)
          total_num = 0
          log_num = 0
          best_eval = {}
          for batch_idx, dev_batch in enumerate(
              grouper(dev_dataloader, len(ctx_l))
          ):
              # Predict for each chunk
              for sample, ctx in zip(dev_batch, ctx_l):
                  if sample is None:
                      continue
                  # Copy the data to device
                  tokens = sample.data.as_in_ctx(ctx)
                  total_num += len(tokens)
                  log_num += len(tokens)
                  segment_ids = (
                      sample.segment_ids.as_in_ctx(ctx)
                      if use_segmentation
                      else None
                  )
                  valid_length = sample.valid_length.as_in_ctx(ctx)
                  p_mask = sample.masks.as_in_ctx(ctx)
                  p_mask = (
                      1 - p_mask
                  )  # In the network, we use 1 --> no_mask, 0 --> mask
                  (
                      start_top_logits,
                      start_top_index,
                      end_top_logits,
                      end_top_index,
                      answerable_logits,
                  ) = qa_net.inference(
                      tokens,
                      segment_ids,
                      valid_length,
                      p_mask,
                      args.start_top_n,
                      args.end_top_n,
                  )
                  for i, qas_id in enumerate(sample.qas_id):
                      result = RawResultExtended(
                          qas_id=qas_id,
                          start_top_logits=start_top_logits[
                              i
                          ].asnumpy(),
                          start_top_index=start_top_index[i].asnumpy(),
                          end_top_logits=end_top_logits[i].asnumpy(),
                          end_top_index=end_top_index[i].asnumpy(),
                          answerable_logits=answerable_logits[
                              i
                          ].asnumpy(),
                      )
  
                      all_results.append(result)
  
              # logging
              if (batch_idx + 1) % log_interval == 0:
                  # Output the loss of per step
                  toc = time.time()
                  logging.info(
                      "[batch {}], Time cost={:.2f},"
                      " Throughput={:.2f} samples/s, ETA={:.2f}h".format(
                          batch_idx + 1,
                          toc - tic,
                          log_num / (toc - tic),
                          (epoch_size - total_num)
                          / (total_num / (toc - epoch_tic))
                          / 3600,
                      )
                  )
                  tic = time.time()
                  log_num = 0
  
          epoch_toc = time.time()
          logging.info(
              "Time cost=%2f s, Thoughput=%.2f samples/s",
              epoch_toc - epoch_tic,
              total_num / (epoch_toc - epoch_tic),
          )
  
          all_predictions = collections.OrderedDict()
          all_nbest_json = collections.OrderedDict()
          no_answer_score_json = collections.OrderedDict()
          for index, (left_index, right_index) in enumerate(
              zip(dev_chunk_feature_ptr[:-1], dev_chunk_feature_ptr[1:])
          ):
              chunked_features = dev_all_chunk_features[
                  left_index:right_index
              ]
              results = all_results[left_index:right_index]
              original_feature = dev_features[index]
              qas_ids = set(
                  [result.qas_id for result in results]
                  + [feature.qas_id for feature in chunked_features]
              )
              assert (
                  len(qas_ids) == 1
              ), "Mismatch Occured between features and results"
              example_qas_id = list(qas_ids)[0]
              assert (
                  example_qas_id == original_feature.qas_id
              ), "Mismatch Occured between original feature and chunked features"
              (
                  not_answerable_score,
                  best_pred,
                  nbest_json,
              ) = predict_extended(
                  original_feature=original_feature,
                  chunked_features=chunked_features,
                  results=results,
                  n_best_size=args.n_best_size,
                  max_answer_length=args.max_answer_length,
                  start_top_n=args.start_top_n,
                  end_top_n=args.end_top_n,
              )
              no_answer_score_json[
                  example_qas_id
              ] = not_answerable_score
              all_predictions[example_qas_id] = best_pred
              all_nbest_json[example_qas_id] = nbest_json
  
          if args.version == "2.0":
              exact = "best_exact"
              f1 = "best_f1"
              na_prob = no_answer_score_json
          else:
              exact = "exact"
              f1 = "f1"
              na_prob = None
  
          cur_eval, revised_predictions = squad_eval(
              dev_dataset,
              all_predictions,
              na_prob,
              revise=na_prob is not None,
          )
          logging.info(
              "The evaluated results are {}".format(
                  json.dumps(cur_eval)
              )
          )
  
          cur_metrics = 0.5 * (cur_eval[exact] + cur_eval[f1])
          if best_eval:
              best_metrics = 0.5 * (best_eval[exact] + best_eval[f1])
          else:
              best_metrics = 0.0
  
          if cur_metrics > best_metrics:
              logging.info(
                  "The evaluated files are saved in {}".format(
                      args.output_dir
                  )
              )
              output_prediction_file = os.path.join(
                  args.output_dir, "predictions.json"
              )
              output_nbest_file = os.path.join(
                  args.output_dir, "nbest_predictions.json"
              )
              na_prob_file = os.path.join(
                  args.output_dir, "na_prob.json"
              )
              revised_prediction_file = os.path.join(
                  args.output_dir, "revised_predictions.json"
              )
  
              with open(output_prediction_file, "w") as of:
                  of.write(json.dumps(all_predictions, indent=4) + "\n")
              with open(output_nbest_file, "w") as of:
                  of.write(json.dumps(all_nbest_json, indent=4) + "\n")
              with open(na_prob_file, "w") as of:
                  of.write(
                      json.dumps(no_answer_score_json, indent=4) + "\n"
                  )
              with open(revised_prediction_file, "w") as of:
                  of.write(
                      json.dumps(revised_predictions, indent=4) + "\n"
                  )
  
              best_eval = cur_eval
              best_eval.update({"best_ckpt": "mybest"})
          return best_eval["best_f1"] / 100
  
      qa_net.load_parameters(
          args.param_checkpoint, ctx=ctx_l, cast_dtype=True
      )
      data_example = next(iter(dev_dataloader))
  
      qa_net.backbone.optimize_for(*data_example, backend="ONEDNN")
      quantized_backbone = quantize_and_calibrate(qa_net)
      best_eval = eval_validation(quantized_backbone)
  
      logging.info(
          "The best evaluated results are {}".format(
              json.dumps(best_eval)
          )
      )
      output_eval_results_file = os.path.join(
          args.output_dir, "best_results.json"
      )
      with open(output_eval_results_file, "w") as of:
          of.write(json.dumps(best_eval, indent=4) + "\n")
      return best_eval
  
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
  from neural_compressor.experimental import Quantization
  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, get_squad_examples_from_json
  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
  import custom_strategy
label_desc: Use image node_id 6 for calling a global function with example usage: evaluate(args, last) and returns: best_eval, net, unknown, self, list, list