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"]
          # calculate loss per sample
          b_logits = output["logits"]
          shift_logits = b_logits[..., :-1, :].contiguous()
          shift_labels = labels[..., 1:].contiguous()
          unreduced_loss = self.unreduced_loss_fct(
              shift_logits.view(-1, shift_logits.size(-1)),
              shift_labels.view(-1),
          )
          loss_per_sample = torch.mean(
              unreduced_loss.view(shift_labels.size()), dim=-1
          )
      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,
          )
  
          prompt_ids = self.get_virtual_prompt_ids_for_megatron_gpt(
              input_ids
          )
  
          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 = (
              self.get_prompt_token_labels_for_megatron_gpt(
                  input_ids, num_prompt_tokens
              )
          )
  
          input_ids_new = torch.cat(
              [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
          )
          loss_per_sample = self.mask_and_reduce_loss_per_sample(
              labels_mask, unmasked_unreduced_loss
          )
  
      return loss, loss_per_sample
  
imports: import collections
  import copy
  import os
  import random
  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 import DialogueGPTClassificationDataset, DialogueSGDDataProcessor
  from nemo.collections.nlp.data.dialogue.data_processor.assistant_data_processor import DialogueAssistantDataProcessor
  from nemo.collections.nlp.data.dialogue.data_processor.design_data_processor import DialogueDesignDataProcessor
  from nemo.collections.nlp.metrics.classification_report import ClassificationReport
  from nemo.collections.nlp.metrics.dialogue_metrics import DialogueClassificationMetrics
  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.collections.nlp.modules.common import VirtualPromptSource, VirtualPromptStyle
  from nemo.collections.nlp.modules.common.text_generation_utils import get_default_sampling_params, megatron_gpt_generate
  from nemo.collections.nlp.modules.common.transformer.text_generation import LengthParam
  from nemo.collections.nlp.parts.nlp_overrides import NLPSaveRestoreConnector
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 13 for calling the DialogueGPTClassificationModel obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, labels, inference) and returns: loss,  loss_per_sample