code: def prepare_crossencoder_mentions(
      tokenizer,
      samples,
      max_context_length=32,
      mention_key="mention",
      context_key="context",
      ent_start_token=ENT_START_TAG,
      ent_end_token=ENT_END_TAG,
  ):
      context_input_list = []  # samples X 128
  
      for sample in tqdm(samples):
          context_tokens = data.get_context_representation(
              sample,
              tokenizer,
              max_context_length,
              mention_key,
              context_key,
              ent_start_token,
              ent_end_token,
          )
          tokens_ids = context_tokens["ids"]
          context_input_list.append(tokens_ids)
  
      context_input_list = np.asarray(context_input_list)
      return context_input_list
  
file_path: BLINK/blink/crossencoder/data_process.py
imports: import torch
  import sys
  import numpy
  from tqdm import tqdm
  import blink.biencoder.data_process
  from blink.common.params import ENT_START_TAG, ENT_END_TAG
label_desc: Use image node_id 1 for calling a global function with example usage: prepare_crossencoder_mentions(tokenizer, samples, max_context_length, mention_key, context_key, ent_start_token, ent_end_token) and returns: context_input_list