code: def test_step(self, batch, batch_idx):
      loss = self.eval_step_helper(batch=batch, mode="test")
      self.test_step_outputs.append(loss)
      return loss
  
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 8 for calling the DialogueGPTClassificationModel obj's underlying member method code with example usage: obj.test_step(batch, batch_idx) and returns: loss