code: def forward(self, context_input, cand_input, label_input=None):
      flag = label_input is None
      scores = self.score_candidate(context_input, cand_input, flag)
      bs = scores.size(0)
      if label_input is None:
          target = torch.LongTensor(torch.arange(bs))
          target = target.to(self.device)
          loss = F.cross_entropy(scores, target, reduction="mean")
      else:
          loss_fct = nn.BCEWithLogitsLoss(reduction="mean")
          # TODO: add parameters?
          loss = loss_fct(scores, label_input)
      return loss, scores
  
imports: import os
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from tqdm import tqdm
  from pytorch_transformers.modeling_bert import BertPreTrainedModel, BertConfig, BertModel
  from pytorch_transformers.tokenization_bert import BertTokenizer
  from blink.common.ranker_base import BertEncoder, get_model_obj
  from blink.common.optimizer import get_bert_optimizer
label_desc: Use image node_id 9 for calling the BiEncoderRanker obj's underlying member method code with example usage: obj.forward(context_input, cand_input, label_input) and returns: loss,  scores