code: def eval_step_helper(self, batch, mode="val"):
      input_ids, attn_masks, labels = batch
  
      loss = self(input_ids, attn_masks, labels)
      self.log(
          "{}_loss".format(mode),
          loss,
          on_step=True,
          on_epoch=True,
          prog_bar=True,
          logger=True,
      )
  
      generated_field, ground_truth_field = self.generate_candidates(
          input_ids, attn_masks, labels
      )
  
      return {
          "loss": loss,
          "input": self.tokenizer.tokenizer.batch_decode(
              input_ids, skip_special_tokens=True
          ),
          "generated_field": generated_field,
          "ground_truth_field": ground_truth_field,
      }
  
imports: import os
  from typing import Dict, Optional, Union
  import numpy
  import torch
  from omegaconf import DictConfig, OmegaConf, open_dict
  from pytorch_lightning import Trainer
  from torch.utils.data import DataLoader
  from transformers import AutoModelForSeq2SeqLM
  from nemo.collections.nlp.data.dialogue import DialogueSGDDataProcessor
  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_s2s_generation_dataset import DialogueS2SGenerationDataset
  from nemo.collections.nlp.metrics.dialogue_metrics import DialogueGenerationMetrics
  from nemo.collections.nlp.models.language_modeling.megatron_t5_model import MegatronT5Model
  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 DialogueS2SGenerationModel obj's underlying member method code with example usage: obj.eval_step_helper(batch, mode) and returns: dict