docstr: Lightning calls this inside the test loop with the data from the test dataloader
  passed in as `batch`.
docstr_tok: ["Lightning","calls","this","inside","the","test","loop","with","the","data","from","the","test","dataloader","passed","in","as","`","batch","`","."]
code: def test_step(self, batch, batch_idx):
      """
      Lightning calls this inside the test loop with the data from the test dataloader
      passed in as `batch`.
      """
      loss = self.validation_step(batch, batch_idx)
      self.test_step_outputs.append(loss)
      return loss
  
imports: import os
  import pathlib
  from typing import Dict, List, Optional
  import torch
  from omegaconf import DictConfig, OmegaConf
  from pytorch_lightning import Trainer
  from torch.utils.data import DataLoader
  from nemo.collections.common.losses import AggregatorLoss, CrossEntropyLoss
  from nemo.collections.nlp.data.intent_slot_classification import IntentSlotClassificationDataset, IntentSlotDataDesc, IntentSlotInferenceDataset
  from nemo.collections.nlp.metrics.classification_report import ClassificationReport
  from nemo.collections.nlp.models.nlp_model import NLPModel
  from nemo.collections.nlp.modules.common import SequenceTokenClassifier
  from nemo.collections.nlp.parts.utils_funcs import tensor2list
  from nemo.core.classes import typecheck
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 12 for calling the IntentSlotClassificationModel obj's underlying member method code with example usage: obj.test_step(batch, batch_idx) and returns: loss