code: def forward(
      self,
      token_idx_ctxt,
      segment_idx_ctxt,
      mask_ctxt,
  ):
      embedding_ctxt = self.encoder(
          token_idx_ctxt, segment_idx_ctxt, mask_ctxt
      )
      return embedding_ctxt.squeeze(-1)
  
imports: import os
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from collections import OrderedDict
  from tqdm import tqdm
  from pytorch_transformers.modeling_utils import CONFIG_NAME, WEIGHTS_NAME
  from pytorch_transformers.modeling_bert import BertPreTrainedModel, BertConfig, BertModel
  from pytorch_transformers.modeling_roberta import RobertaConfig, RobertaModel
  from pytorch_transformers.tokenization_bert import BertTokenizer
  from pytorch_transformers.tokenization_roberta import RobertaTokenizer
  from blink.common.ranker_base import BertEncoder, get_model_obj
  from blink.common.optimizer import get_bert_optimizer
  from blink.common.params import ENT_START_TAG, ENT_END_TAG, ENT_TITLE_TAG
label_desc: Use image node_id 2 for calling the CrossEncoderModule obj's underlying member method code with example usage: obj.forward(token_idx_ctxt, segment_idx_ctxt, mask_ctxt) and returns: embedding_ctxt