class_docstr: BERT model with a QA (token classification) head 
class_docstr_tok: ["BERT","model","with","a","QA","(","token","classification",")","head"]
code: def test_step(self, batch, batch_idx):
      return self.validation_step(batch, batch_idx)
  
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 4 for calling the BERTQAModel obj's underlying member method code with example usage: obj.test_step(batch, batch_idx) and returns: self