code: def process_single_text(
      raw_text, max_seq_length, encoder, BOS_token, EOS_token, PAD_token
  ):
      """Processes a single piece of text. Performs BPE encoding,
      converting to indexes, truncation, and padding, etc.
      """
      # BPE
      tokens = encoder.encode(raw_text)
  
      # Truncate
      max_len = max_seq_length
      if BOS_token is not None and len(BOS_token) > 0:
          max_len -= 1
      if EOS_token is not None and len(EOS_token) > 0:
          max_len -= 1
      tokens = tokens[:max_len]
  
      # Append special tokens
      if BOS_token is not None and len(BOS_token) > 0:
          tokens = [encoder.encoder[BOS_token]] + tokens
      if EOS_token is not None and len(EOS_token) > 0:
          tokens = tokens + [encoder.encoder[EOS_token]]
  
      token_length = len(tokens)
  
      # Pad
      PAD_token_id = encoder.encoder[PAD_token]
      while len(tokens) < max_seq_length:
          tokens.append(PAD_token_id)
  
      assert len(tokens) == max_seq_length
  
      return tokens, token_length
  
imports: import os
  import collections
  import tensorflow
label_desc: Use image node_id 1 for calling a global function with example usage: process_single_text(raw_text, max_seq_length, encoder, BOS_token, EOS_token, PAD_token) and returns: tokens,  token_length