code: def forward(self, input_ids, attention_mask, labels, inference=True):
      if self.cfg.library == "huggingface":
          output = self.language_model(
              input_ids=input_ids,
              attention_mask=attention_mask,
              labels=labels,
          )
          loss = output["loss"]
  
      elif self.cfg.library == "megatron":
          num_prompt_tokens = (
              len(self.language_model.pseudo_token_ids)
              if hasattr(self.language_model, "pseudo_token_ids")
              else 0
          )
  
          position_ids = torch.arange(
              start=0,
              end=num_prompt_tokens + input_ids.size(1),
              dtype=torch.long,
              device=input_ids.device,
          )
  
          position_ids = position_ids.unsqueeze(0).repeat(
              input_ids.size(0), 1
          )
  
          prompt_ids = (
              torch.tensor([0] * input_ids.size(0))
              if self.prompt_learning
              else None
          )
  
          attn_mask_add_on = torch.ones(
              (attention_mask.size(0), num_prompt_tokens),
              device=attention_mask.device,
          )
          full_attention_mask = torch.cat(
              [attn_mask_add_on, attention_mask], axis=-1
          )
          full_attention_mask_expand = torch.tril(
              full_attention_mask.unsqueeze(2).tile(
                  full_attention_mask.size(1)
              )
          ).unsqueeze(1)
  
          attn_mask = full_attention_mask_expand > 0
  
          prompt_token_labels = torch.full(
              size=(input_ids.size(0), num_prompt_tokens),
              fill_value=self.tokenizer.tokenizer.pad_token_id,
              dtype=torch.long,
          )
  
          if self.prompt_learning:
              prompt_token_labels.data = torch.LongTensor(
                  np.tile(
                      np.array(self.language_model.pseudo_token_ids),
                      (input_ids.size(0), 1),
                  )
              )
  
          prompt_token_labels = prompt_token_labels.to(input_ids.device)
  
          input_ids_new = torch.cat(
              [torch.zeros_like(prompt_token_labels), input_ids], axis=1
          )
          make_up_last_column_input_ids = (
              torch.ones_like(input_ids_new[:, -1:])
              * self.tokenizer.tokenizer.pad_token_id
          )
          left_shifted_input_ids = torch.cat(
              [input_ids_new[:, 1:], make_up_last_column_input_ids],
              axis=-1,
          )
          if self.prompt_learning:
              unmasked_unreduced_loss = self.language_model(
                  input_ids_new,
                  position_ids,
                  attn_mask,
                  labels=left_shifted_input_ids,
                  taskname_ids=prompt_ids,
                  inference=inference,
              )
          else:
              unmasked_unreduced_loss = self.language_model(
                  input_ids,
                  position_ids,
                  attn_mask,
                  labels=left_shifted_input_ids,
              )
  
          if isinstance(unmasked_unreduced_loss, tuple):
              unmasked_unreduced_loss = unmasked_unreduced_loss[0]
  
          labels = torch.cat([prompt_token_labels, labels], axis=1)
          make_up_last_column_labels = (
              torch.ones_like(labels[:, -1:])
              * self.tokenizer.tokenizer.pad_token_id
          )
          new_labels = torch.cat(
              [labels[:, 1:], make_up_last_column_labels], axis=-1
          )
          filler = torch.zeros_like(new_labels)
          labels_mask_0 = torch.where(
              new_labels != -100, new_labels, filler
          )
          labels_mask = labels_mask_0 > 0
  
          loss = self.mask_and_reduce_loss(
              labels_mask, unmasked_unreduced_loss
          )
      return loss
  
imports: import copy
  import os
  from typing import Dict, Optional, Union
  import numpy
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from torch.utils.data import DataLoader
  from transformers import AutoModelWithLMHead
  from nemo.collections.nlp.data.dialogue.data_processor.mellon_qa_data_processor import DialogueMellonQADataProcessor
  from nemo.collections.nlp.data.dialogue.data_processor.ms_marco_data_processor import DialogueMSMarcoDataProcessor
  from nemo.collections.nlp.data.dialogue.dataset.dialogue_gpt_generation_dataset import DialogueGPTGenerationDataset
  from nemo.collections.nlp.metrics.dialogue_metrics import DialogueGenerationMetrics
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_model import MegatronGPTModel
  from nemo.collections.nlp.models.language_modeling.megatron_gpt_prompt_learning_model import MegatronGPTPromptLearningModel
  from nemo.collections.nlp.models.nlp_model import NLPModel
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 9 for calling the DialogueGPTGenerationModel obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, labels, inference) and returns: loss