code: def forward(self, input_ids, attention_mask, labels=None):
      output = self.roberta(input_ids.squeeze())
      output = self.classifier(output.pooler_output)
      output = torch.sigmoid(output)
      loss = 0
      if labels is not None:
          loss = self.criterion(output, labels)
      return loss, output
  
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 2 for calling the BertForMultilabelClassification obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, labels) and returns: loss,  output