code: def xlm_embeddings_post_processor(
      sequence_embeddings: np.ndarray,
  ) -> Tuple[np.ndarray, np.ndarray]:
      """Post process embeddings from XLM models.
  
      by taking a mean over sequence embeddings and returning that as sentence
      representation. Remove first and last time steps
      corresponding to special tokens from the sequence embeddings.
  
      Args:
          sequence_embeddings: Sequence of token level embeddings received as output from
              XLM
  
      Returns:
          sentence level embedding and post-processed sequence level embedding
      """
      post_processed_embedding = sequence_embeddings[1:-1]
      sentence_embedding = np.mean(post_processed_embedding, axis=0)
  
      return sentence_embedding, post_processed_embedding
  
file_path: rasa/rasa/nlu/utils/hugging_face/transformers_pre_post_processors.py
imports: from typing import List, Tuple, Text
  import numpy
label_desc: Use image node_id 12 for calling a global function with example usage: xlm_embeddings_post_processor(sequence_embeddings) and returns: sentence_embedding,  post_processed_embedding