code: def __init__(
      self,
      cfg: DictConfig,
      trainer: Trainer = None,
  ):
      self.cfg = cfg
      self.eval_mode = cfg.dataset.eval_mode
      self.data_prepared = False
      self.epoch_number = 0
      self.prompt_learning = self.cfg.prompt_learning
      super().__init__(cfg=cfg, trainer=trainer, no_lm_init=True)
  
      if self.cfg.library == "huggingface":
          self.language_model = AutoModelWithLMHead.from_pretrained(
              cfg.language_model.pretrained_model_name
          )
          self.language_model.resize_token_embeddings(
              len(self.tokenizer.tokenizer)
          )
          self.unreduced_loss_fct = torch.nn.CrossEntropyLoss(
              reduction="none"
          )
      elif self.cfg.library == "megatron":
          if self.prompt_learning:
              if os.path.exists(cfg.prompt_learning_nemo_path):
                  self.language_model = MegatronGPTPromptLearningModel.restore_from(
                      cfg.prompt_learning_nemo_path,
                      trainer=trainer,
                      save_restore_connector=NLPSaveRestoreConnector(),
                  )
              else:
                  # removing tokenizer cfg as this triggers tokenizer construction which is not helpful here as we have a separate tokenizer
                  new_cfg = copy.copy(cfg)
                  del new_cfg.tokenizer
                  new_cfg.nemo_path = cfg.prompt_learning_nemo_path
                  self.language_model = MegatronGPTPromptLearningModel(
                      new_cfg, trainer
                  )
          else:
              self.language_model = MegatronGPTModel.restore_from(
                  cfg.language_model.lm_checkpoint, trainer=trainer
              )
  
      all_labels = list(
          self._train_dl.dataset.all_possible_labels.union(
              self._validation_dl.dataset.all_possible_labels,
              self._test_dl.dataset.all_possible_labels,
          )
      )
      self.label_to_ids = collections.defaultdict(int)
  
      for i in range(len(all_labels)):
          self.label_to_ids[all_labels[i]] = i
  
      self.all_existing_labels = set(self.label_to_ids.keys())
  
      self.token_to_words = {}
      self.classification_report = ClassificationReport(
          num_classes=len(self.label_to_ids) + 1,
          mode="micro",
          label_ids=self.label_to_ids,
          dist_sync_on_step=True,
      )
  
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 1 to create a new DialogueGPTClassificationModel object from inherited base classes: NLPModel with example: obj = DialogueGPTClassificationModel(cfg, trainer)