code: def generate_candidates(
      self, labels, template_length, input_ids, attn_masks
  ):
      tokens_to_generate = self.cfg.tokens_to_generate
  
      if self.cfg.library == "huggingface":
          generated_tokens = []
          max_length = 0
          for i in range(input_ids.size(0)):
              param_dict = {
                  "input_ids": input_ids[
                      i : i + 1, : template_length[i]
                  ],
                  "max_length": template_length[i] + tokens_to_generate,
                  "pad_token_id": self.tokenizer.tokenizer.pad_token_id,
              }
              generated_tokens.append(
                  self.language_model.generate(**param_dict)
              )
              max_length = max(max_length, generated_tokens[-1].size(1))
  
          # pad each generated to ensure they are of same length in dim 1, therefore stack-able
          generated_tokens = [
              torch.cat(
                  [
                      i,
                      torch.ones((1, max_length - i.size(1))).to(
                          i.device
                      )
                      * self.tokenizer.tokenizer.pad_token_id,
                  ],
                  axis=-1,
              )
              for i in generated_tokens
          ]
          generated_tokens = torch.cat(generated_tokens, axis=0)
          num_prompt_tokens = 0
  
      elif self.cfg.library == "megatron":
          prompt_ids = self.get_virtual_prompt_ids_for_megatron_gpt(
              input_ids
          )
  
          num_prompt_tokens = (
              len(self.language_model.pseudo_token_ids)
              if hasattr(self.language_model, "pseudo_token_ids")
              else 0
          )
  
          prompt_token_labels = (
              self.get_prompt_token_labels_for_megatron_gpt(
                  input_ids, num_prompt_tokens
              )
          )
          input_ids_without_answers = [
              torch.cat(
                  [
                      input_ids[i, : template_length[i]],
                      torch.ones(
                          (
                              input_ids.size(1)
                              - template_length[i].item(),
                          )
                      ).to(input_ids.device)
                      * self.tokenizer.tokenizer.pad_token_id,
                  ],
                  axis=-1,
              ).type(input_ids.dtype)
              for i in range(input_ids.size(0))
          ]
          input_ids_without_answers = torch.stack(
              input_ids_without_answers
          )
          input_ids_new = torch.cat(
              [
                  prompt_token_labels,
                  input_ids_without_answers,
                  torch.ones(
                      (input_ids.size(0), tokens_to_generate)
                  ).to(input_ids.device)
                  * self.tokenizer.tokenizer.pad_token_id,
              ],
              axis=1,
          ).type(input_ids.dtype)
  
          tokens_for_generation = (
              input_ids_new,
              template_length + num_prompt_tokens,
          )
  
          length_param: LengthParam = {
              "min_length": 0,
              "max_length": tokens_to_generate,
          }
  
          generated_dict = megatron_gpt_generate(
              self.language_model,
              tokens_for_generation,
              self.tokenizer,
              length_param,
              get_default_sampling_params(),
              task_ids=prompt_ids,
          )
          generated_tokens = torch.LongTensor(
              generated_dict["token_ids"]
          )
  
      (
          generated_field,
          ground_truth_field,
      ) = self.process_into_structured_fields(
          generated_tokens,
          labels,
          template_length=template_length + num_prompt_tokens,
      )
      return generated_field, ground_truth_field
  
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 20 for calling the DialogueGPTClassificationModel obj's underlying member method code with example usage: obj.generate_candidates(labels, template_length, input_ids, attn_masks) and returns: generated_field,  ground_truth_field