class_docstr: BERT language model pretraining.
class_docstr_tok: ["BERT","language","model","pretraining","."]
code: def setup_test_data(self, test_data_config: Optional[DictConfig]):
      pass
  
imports: import os
  from typing import Dict, 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, SmoothedCrossEntropyLoss
  from nemo.collections.common.metrics import Perplexity
  from nemo.collections.nlp.data.language_modeling.lm_bert_dataset import BertPretrainingDataset, BertPretrainingPreprocessedDataloader
  from nemo.collections.nlp.modules.common import BertPretrainingTokenClassifier, SequenceClassifier
  from nemo.collections.nlp.modules.common.lm_utils import get_lm_model
  from nemo.collections.nlp.modules.common.tokenizer_utils import get_tokenizer
  from nemo.core.classes.common import PretrainedModelInfo, typecheck
  from nemo.core.classes.modelPT import ModelPT
  from nemo.core.neural_types import NeuralType
  from nemo.utils import logging
label_desc: Use image node_id 12 for calling the BERTLMModel obj's underlying member method code with example usage: obj.setup_test_data(test_data_config) without return types