code: def __init__(self, cfg: DictConfig, trainer: Trainer = None):
      self.cfg = cfg
  
      self.setup_tokenizer(cfg.tokenizer)
      self.tokenizer.tokenizer.pad_token = (
          self.tokenizer.tokenizer.eos_token
      )
      self.epoch_number = 0
      super().__init__(cfg=cfg, trainer=trainer, no_lm_init=True)
  
      if self.cfg.library == "huggingface":
          self.language_model = AutoModelForCausalLM.from_pretrained(
              cfg.language_model.pretrained_model_name
          )
          self.language_model.resize_token_embeddings(
              len(self.tokenizer.tokenizer)
          )
          if self.cfg.language_model.lm_checkpoint:
              self.language_model.load_state_dict(
                  torch.load(self.cfg.language_model.lm_checkpoint)
              )
      elif self.cfg.library == "megatron":
          self.language_model = MegatronGPTModel.restore_from(
              cfg.language_model.lm_checkpoint, trainer=trainer
          )
  
imports: import collections
  from typing import List, Optional
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from transformers import AutoModelForCausalLM
  from nemo.collections.nlp.data.question_answering.data_processor.qa_processing import QAProcessor
  from nemo.collections.nlp.data.question_answering.dataset.qa_gpt_dataset import GPTQADataset
  from nemo.collections.nlp.metrics.qa_metrics import QAMetrics
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
  from nemo.collections.nlp.models.question_answering.qa_base_model import BaseQAModel
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.utils import logging
label_desc: Use image node_id 1 to create a new GPTQAModel object from inherited base classes: BaseQAModel with example: obj = GPTQAModel(cfg, trainer)