code: def __init__(self, cfg: DictConfig, trainer: Trainer = None):
      self.cfg = cfg
  
      if self.cfg.library == "huggingface":
          self.setup_tokenizer(cfg.tokenizer)
      elif self.cfg.library == "megatron":
          # supporting MegatronT5Model in precision = fp16
          t5_cfg = MegatronT5Model.restore_from(
              restore_path=cfg.language_model.lm_checkpoint,
              trainer=trainer,
              return_config=True,
          )
          # Override the T5 configuration with the one from the config file.
          OmegaConf.set_struct(t5_cfg, True)
          with open_dict(t5_cfg):
              t5_cfg.masked_softmax_fusion = False
              t5_cfg.precision = 16
  
          language_model = MegatronT5Model.restore_from(
              restore_path=cfg.language_model.lm_checkpoint,
              trainer=trainer,
              override_config_path=t5_cfg,
          )
          self.tokenizer = language_model.tokenizer
  
      super().__init__(cfg=cfg, trainer=trainer, no_lm_init=True)
  
      if self.cfg.library == "huggingface":
          self.language_model = AutoModelForSeq2SeqLM.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 = language_model
  
imports: import collections
  from typing import List, Optional
  import torch
  from omegaconf import DictConfig, OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from torch.cuda.amp import autocast
  from transformers import AutoModelForSeq2SeqLM
  from nemo.collections.nlp.data.question_answering.data_processor.qa_processing import QAProcessor
  from nemo.collections.nlp.data.question_answering.dataset.qa_s2s_dataset import S2SQADataset
  from nemo.collections.nlp.metrics.qa_metrics import QAMetrics
  from nemo.collections.nlp.models.language_modeling.megatron_t5_model import MegatronT5Model
  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 S2SQAModel object from inherited base classes: BaseQAModel with example: obj = S2SQAModel(cfg, trainer)