class_docstr: BERT model with a QA (token classification) head 
class_docstr_tok: ["BERT","model","with","a","QA","(","token","classification",")","head"]
code: def evaluate(
      self,
      features: List,
      examples: List,
      processor: object,
      unique_ids: List[str],
      start_logits: List[List[float]],
      end_logits: List[List[float]],
      n_best_size: int,
      max_answer_length: int,
      do_lower_case: bool,
      version_2_with_negative: bool,
      null_score_diff_threshold: float,
  ):
      (
          all_predictions,
          all_nbest_json,
          scores_diff_json,
      ) = self.get_predictions(
          features,
          examples,
          processor,
          unique_ids,
          start_logits,
          end_logits,
          n_best_size,
          max_answer_length,
          do_lower_case,
          version_2_with_negative,
          null_score_diff_threshold,
      )
  
      eval_results = QAMetrics.evaluate_predictions(
          examples, all_predictions
      )
  
      return eval_results, all_predictions, all_nbest_json
  
imports: import collections
  from typing import List, Optional
  import numpy
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from transformers.models.bert.tokenization_bert import BasicTokenizer
  from nemo.collections.common.losses import SpanningLoss
  from nemo.collections.common.parts.utils import _compute_softmax
  from nemo.collections.nlp.data.question_answering.data_processor.qa_processing import QAProcessor
  from nemo.collections.nlp.data.question_answering.dataset.qa_bert_dataset import BERTQADataset
  from nemo.collections.nlp.metrics.qa_metrics import QAMetrics
  from nemo.collections.nlp.models.question_answering.qa_base_model import BaseQAModel
  from nemo.collections.nlp.modules.common import TokenClassifier
  from nemo.collections.nlp.parts.utils_funcs import tensor2list
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.utils import logging
label_desc: Use image node_id 9 for calling the BERTQAModel obj's underlying member method code with example usage: obj.evaluate(features, examples, processor, unique_ids, start_logits, end_logits, n_best_size, max_answer_length, do_lower_case, version_2_with_negative, null_score_diff_threshold) and returns: eval_results,  all_predictions,  all_nbest_json