class_docstr: TextClassificationModel to be trained on two- or three-class textual entailment data, to be used for zero shot intent recognition.
class_docstr_tok: ["TextClassificationModel","to","be","trained","on","two-","or","three-class","textual","entailment","data",",","to","be","used","for","zero","shot","intent","recognition","."]
code: def setup_test_data(self, test_data_config: Optional[DictConfig]):
      if not test_data_config or not test_data_config.file_name:
          logging.info(
              f"Dataloader config or file_path for the test data set is missing, so no data loader for test is created!"
          )
          self._test_dl = None
          return
      self._test_dl = self._setup_dataloader_from_config(
          cfg=test_data_config
      )
  
imports: import os
  from typing import Dict, List, Optional, Union
  import numpy
  import torch
  from omegaconf import DictConfig
  from pytorch_lightning import Trainer
  from nemo.collections.nlp.data.zero_shot_intent_recognition.zero_shot_intent_dataset import ZeroShotIntentDataset, ZeroShotIntentInferenceDataset, calc_class_weights_from_dataloader
  from nemo.collections.nlp.models import TextClassificationModel
  from nemo.core.classes.common import PretrainedModelInfo
  from nemo.utils import logging
label_desc: Use image node_id 5 for calling the ZeroShotIntentModel obj's underlying member method code with example usage: obj.setup_test_data(test_data_config) without return types