code: def __init__(
      self,
      data: BertQADataBunch,
      model: torch.nn.Module,
      pretrained_model_path,
      output_dir,
      device,
      logger,
      multi_gpu=True,
      is_fp16=True,
      warmup_steps=0,
      fp16_opt_level="O1",
      grad_accumulation_steps=1,
      max_grad_norm=1.0,
      adam_epsilon=1e-8,
      logging_steps=100,
  ):
      super(BertQALearner, self).__init__(
          data,
          model,
          pretrained_model_path,
          output_dir,
          device,
          logger,
          multi_gpu,
          is_fp16,
          warmup_steps,
          fp16_opt_level,
          grad_accumulation_steps,
          max_grad_norm,
          adam_epsilon,
          logging_steps,
      )
  
      self.validation_out = self.output_dir / "validation_out"
      self.validation_out.mkdir(exist_ok=True)
  
imports: import os
  import torch
  import pandas
  import numpy
  from pathlib import Path
  import collections
  import math
  from io import open
  import json
  from .data_qa import BertQADataBunch
  from .learner_util import Learner
  from fastprogress.fastprogress import master_bar, progress_bar
  from tensorboardX import SummaryWriter
  from transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
  from transformers import BertConfig, BertForQuestionAnswering, XLNetConfig, XLNetForQuestionAnswering, XLMConfig, XLMForQuestionAnswering, DistilBertConfig, DistilBertForQuestionAnswering
  from .utils_squad_evaluate import EVAL_OPTS, main
label_desc: Use image node_id 2 to create a new BertQALearner object from inherited base classes: Learner with example: obj = BertQALearner(data, model, pretrained_model_path, output_dir, device, logger, multi_gpu, is_fp16, warmup_steps, fp16_opt_level, grad_accumulation_steps, max_grad_norm, adam_epsilon, logging_steps)