class_docstr: BERT model with a QA (token classification) head 
class_docstr_tok: ["BERT","model","with","a","QA","(","token","classification",")","head"]
code: def __init__(self, cfg: DictConfig, trainer: Trainer = None):
      super().__init__(cfg=cfg, trainer=trainer, no_lm_init=False)
      self.classifier = TokenClassifier(
          hidden_size=self.hidden_size,
          num_classes=cfg.token_classifier.num_classes,
          num_layers=cfg.token_classifier.num_layers,
          activation=cfg.token_classifier.activation,
          log_softmax=cfg.token_classifier.log_softmax,
          dropout=cfg.token_classifier.dropout,
          use_transformer_init=cfg.token_classifier.use_transformer_init,
      )
  
      self.loss = SpanningLoss()
  
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 1 to create a new BERTQAModel object from inherited base classes: BaseQAModel with example: obj = BERTQAModel(cfg, trainer)