code: def __init__(self, bert_encoder, config, num_labels: int = 1):
      super(BertClassificationLoss, self).__init__(config)
      self.bert = bert_encoder
      self.num_labels = num_labels
      self.dropout = nn.Dropout(config.hidden_dropout_prob)
      self.classifier = nn.Linear(config.hidden_size, num_labels)
      self.classifier.apply(self.init_bert_weights)
  
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 1 to create a new BertClassificationLoss object from inherited base classes: PreTrainedBertModel with example: obj = BertClassificationLoss(bert_encoder, config, num_labels)