code: def __init__(
      self,
      bert_model,
      n_classes: int,
      n_training_steps=None,
      n_warmup_steps=None,
  ):
      super().__init__()
      self.roberta = RobertaForABSA.from_pretrained(bert_model)
      self.classifier = nn.Linear(
          self.roberta.config.hidden_size, n_classes
      )
      self.n_training_steps = n_training_steps
      self.n_warmup_steps = n_warmup_steps
      self.criterion = nn.BCELoss()
      self.train_f1 = F1(mdmc_average="global")
      self.val_f1 = F1(mdmc_average="global")
      self.test_f1 = F1(mdmc_average="global")
  
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 1 to create a new BertForMultilabelClassification object from inherited base classes: pl with example: obj = BertForMultilabelClassification(bert_model, n_classes, n_training_steps, n_warmup_steps)