code: def forward_candidate(
      self,
      token_idx_cands,
      segment_idx_cands,
      mask_cands,
  ):
      try:
          return self.cand_encoder(
              token_idx_cands, segment_idx_cands, mask_cands
          )
      except:
          print(token_idx_cands.size())
          print(segment_idx_cands.size())
          print(mask_cands.size())
          return torch.rand(token_idx_cands.size()).to(
              token_idx_cands.device
          )
  
imports: import os
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from tqdm import tqdm
  from collections import OrderedDict
  from pytorch_transformers.modeling_bert import BertPreTrainedModel, BertConfig, BertModel
  from pytorch_transformers.tokenization_bert import BertTokenizer
  from elq.common.ranker_base import BertEncoder, get_model_obj
  from blink.common.optimizer import get_bert_optimizer
  from elq.biencoder.allennlp_span_utils import batched_span_select, batched_index_select
  from elq.biencoder.utils import batch_reshape_mask_left
label_desc: Use image node_id 7 for calling the BiEncoderModule obj's underlying member method code with example usage: obj.forward_candidate(token_idx_cands, segment_idx_cands, mask_cands) and returns: self, torch