code: def test_step(self, batch, batch_idx):
      input_ids = batch["input_ids"]
      attention_mask = batch["attention_mask"]
      labels = batch["labels"]
      loss, outputs = self(input_ids, attention_mask, labels)
      self.test_f1(outputs, labels.int())
      self.log("test_f1", self.test_f1, on_step=True, on_epoch=True)
      self.log("test_loss", loss, prog_bar=True, logger=True)
      return loss
  
imports: import torch
  import hydra
  import torch.nn
  import pytorch_lightning
  from omegaconf import DictConfig
  from torchmetrics import F1Score
  from torch.utils.data import Dataset, DataLoader
  from transformers import AutoTokenizer, AdamW, RobertaModel, BertPreTrainedModel, RobertaConfig
  from underthesea import word_tokenize
  from underthesea.datasets.uit_absa_hotel.uit_absa_hotel import UITABSAHotel
label_desc: Use image node_id 5 for calling the BertForMultilabelClassification obj's underlying member method code with example usage: obj.test_step(batch, batch_idx) and returns: loss