code: def __init__(self, params):
      super(BiEncoderModule, self).__init__()
      ctxt_bert = BertModel.from_pretrained(params["bert_model"])
      cand_bert = BertModel.from_pretrained(params["bert_model"])
      self.context_encoder = BertEncoder(
          ctxt_bert,
          params["out_dim"],
          layer_pulled=params["pull_from_layer"],
          add_linear=params["add_linear"],
      )
      self.cand_encoder = BertEncoder(
          cand_bert,
          params["out_dim"],
          layer_pulled=params["pull_from_layer"],
          add_linear=params["add_linear"],
      )
      self.config = ctxt_bert.config
  
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 1 to create a new BiEncoderModule object from inherited base classes: torch.nn with example: obj = BiEncoderModule(params)