code: def forward(self, input_ids, input_attn_mask, labels):
      loss, per_sample_perplexity = None, None
      if self.cfg.library == "huggingface":
          with autocast(enabled=False):
              output = self.language_model(
                  input_ids=input_ids,
                  attention_mask=input_attn_mask,
                  labels=labels,
              )
          loss = output["loss"]
          lm_logits = output["logits"]
          per_sample_perplexity = self._get_per_sample_perplexity(
              lm_logits, labels
          )
  
      elif self.cfg.library == "megatron":
          labels = torch.where(
              labels != -100, labels, torch.zeros_like(labels)
          )
          output_attn_masks = torch.where(
              labels > 0,
              torch.ones_like(labels),
              torch.zeros_like(labels),
          )
          unmasked_unreduced_loss = self.language_model(
              input_ids,
              labels[:, :-1],
              input_attn_mask,
              output_attn_masks[:, :-1],
              lm_labels=labels[:, 1:],
          )
          loss = self.language_model.loss_func(
              output_attn_masks[:, 1:], unmasked_unreduced_loss
          )
          per_sample_perplexity = torch.exp(unmasked_unreduced_loss)
  
      return loss, per_sample_perplexity
  
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 7 for calling the S2SQAModel obj's underlying member method code with example usage: obj.forward(input_ids, input_attn_mask, labels) and returns: loss,  per_sample_perplexity