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)
  
      elif self.cfg.library == "megatron":
          positions, request = self.prepare_megatron_generation(
              labels, input_ids, template_length
          )
          outputs = self.language_model.complete(
              request, positions, tokens_to_generate
          )
          generated_tokens = self.post_process_megatron_generation(
              outputs
          )
  
      generated_field = self.process_into_structured_fields(
          generated_tokens, template_length=template_length
      )
  
      ground_truth_field = self.process_into_structured_fields(
          labels, template_length=template_length
      )
  
      return generated_field, ground_truth_field
  
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 14 for calling the DialogueGPTGenerationModel 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