code: def forward(
      self,
      input_ids,
      token_type_ids=None,
      attention_mask=None,
      labels=None,
  ):
      _, pooled_output = self.bert(
          input_ids,
          token_type_ids,
          attention_mask,
          output_all_encoded_layers=False,
      )
      pooled_output = self.dropout(pooled_output)
      logits = self.classifier(pooled_output)
  
      if labels is not None:
          loss_fct = MSELoss()
          loss = loss_fct(logits.view(-1, 1), labels.view(-1, 1))
          return loss
      else:
          return logits
  
imports: import torch
  import torch.nn
  from torch.nn import CrossEntropyLoss, MSELoss
  from turing.utils import TorchTuple
  from pytorch_pretrained_bert.modeling import BertModel
  from pytorch_pretrained_bert.modeling import BertPreTrainingHeads, PreTrainedBertModel, BertPreTrainingHeads
  from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
label_desc: Use image node_id 2 for calling the BertRegressionLoss obj's underlying member method code with example usage: obj.forward(input_ids, token_type_ids, attention_mask, labels) and returns: loss, logits