text: from sentence_transformers.cross_encoder import CrossEncoder as CE
  import numpy as np
  from typing import List, Dict, Tuple
  
  class CrossEncoder:
      def __init__(self, model_path: str = None, max_length: int = None, **kwargs):
          if max_length != None:
              self.model = CE(model_path, max_length=max_length, **kwargs)
          self.model = CE(model_path, **kwargs)
      
      def predict(self, sentences: List[Tuple[str,str]], batch_size: int = 32, show_progress_bar: bool = True) -> List[float]:
          return self.model.predict(
              sentences=sentences, 
              batch_size=batch_size, 
              show_progress_bar=show_progress_bar)