class_docstr: BERT model with a QA (token classification) head 
class_docstr_tok: ["BERT","model","with","a","QA","(","token","classification",")","head"]
code: def training_step(self, batch, batch_idx):
      (
          input_ids,
          input_type_ids,
          input_mask,
          unique_ids,
          start_positions,
          end_positions,
      ) = batch
      logits = self.forward(
          input_ids=input_ids,
          token_type_ids=input_type_ids,
          attention_mask=input_mask,
      )
      loss, _, _ = self.loss(
          logits=logits,
          start_positions=start_positions,
          end_positions=end_positions,
      )
      lr = self._optimizer.param_groups[0]["lr"]
  
      self.log("lr", lr, prog_bar=True)
      self.log(
          "train_loss",
          loss,
          on_step=True,
          on_epoch=True,
          prog_bar=True,
          logger=True,
      )
  
      return {"loss": loss, "lr": lr}
  
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 2 for calling the BERTQAModel obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) and returns: dict