code: def forward(self, input_ids, input_attn_mask, labels):
      loss, per_sample_perplexity = None, None
      if self.cfg.library == "huggingface":
          output = self.language_model(
              input_ids=input_ids,
              attention_mask=input_attn_mask,
              labels=labels,
          )
          loss, lm_logits = output["loss"], output["logits"]
          shift_logits = lm_logits[..., :-1, :].contiguous()
          shift_labels = labels[..., 1:].contiguous()
          per_sample_perplexity = self._get_per_sample_perplexity(
              shift_logits, shift_labels
          )
  
      elif self.cfg.library == "megatron":
          raise NotImplementedError()
  
      return loss, per_sample_perplexity
  
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 7 for calling the GPTQAModel obj's underlying member method code with example usage: obj.forward(input_ids, input_attn_mask, labels) and returns: loss,  per_sample_perplexity