code: def evaluate():
      model = build_finetuning_model(
          f"./output/bert_finetuned/{task_name}.bin", is_quant=False
      )
      trainer = prepare_traced_trainer(model, is_quant=False)
      metrics = trainer.evaluate()
      print(f"Evaluate metrics={metrics}")
  
file_path: nni/docs/source/tutorials/quantization_bert_glue.py
imports: from pathlib import Path
  import argparse
  import numpy
  import torch
  from torch.utils.data import ConcatDataset
  import nni
  from datasets import load_dataset, load_metric
  from transformers import BertTokenizerFast, DataCollatorWithPadding, BertForSequenceClassification, EvalPrediction
  from transformers.trainer import Trainer
  from transformers.training_args import TrainingArguments
  import nni
  from nni.compression.quantization import QATQuantizer, LsqQuantizer, PtqQuantizer
  from nni.compression.utils import TransformersEvaluator
label_desc: Use image node_id 6 for calling a global function with example usage: evaluate() without return types