code: def prepare(self, priming_data):
      no_null_sentences = (
          x if x is not None else "" for x in priming_data
      )
      unique_tokens = set()
      max_words_per_sent = 0
      for sent in no_null_sentences:
          tokens = list(tokenize_text(sent))
          max_words_per_sent = max(max_words_per_sent, len(tokens))
          for tok in tokens:
              unique_tokens.add(tok)
  
      self.cae.prepare(pd.Series(list(unique_tokens)), pd.Series([]))
  
      if self._mode == "concat":
          self.max_words_per_sent = max_words_per_sent
          self._combine_fn = self._combine_concat
      elif self._mode == "mean":
          self._combine_fn = self._combine_mean
      else:
          self._unexpected_mode()
  
      self.is_prepared = True
      encoded = self.encode([priming_data[0]])
      self.output_size = len(encoded[0])
  
file_path: lightwood/lightwood/encoder/text/short.py
imports: from typing import List
  import torch
  from lightwood.encoder import BaseEncoder
  from lightwood.encoder.categorical import CategoricalAutoEncoder
  from type_infer.rule_based.helpers import tokenize_text
  from lightwood.helpers.torch import concat_vectors_and_pad, average_vectors
  import pandas
label_desc: Use image node_id 5 for calling the ShortTextEncoder obj's underlying member method code with example usage: obj.prepare(priming_data) without return types