code: def forward(self, input_ids, attention_mask, labels=None):
      outputs = self.transformer(
          input_ids.squeeze(), attention_mask=attention_mask.squeeze()
      )
      sequence_output = outputs[0]
      logits = self.linear(sequence_output)
      loss = None
      if labels is not None:
          active_loss = attention_mask.view(-1) == 1
          active_logits = logits.view(-1, self.num_labels)
          active_labels = torch.where(
              active_loss,
              labels.view(-1),
              torch.tensor(self.train_loss.ignore_index).type_as(
                  labels
              ),
          )
          loss = self.train_loss(active_logits, active_labels)
      return loss, logits
  
imports: import logging
  from os.path import join
  import pytorch_lightning
  from pytorch_lightning.loggers import WandbLogger
  from torch.optim import AdamW
  import torch.nn
  import torch
  from torchmetrics import F1
  from torch.utils.data import DataLoader, Dataset
  from transformers import AutoTokenizer, AutoModel
label_desc: Use image node_id 2 for calling the BertForTokenClassification obj's underlying member method code with example usage: obj.forward(input_ids, attention_mask, labels) and returns: loss,  logits