class_docstr: Tokenizing strategy for Reflection prompts.
class_docstr_tok: ["Tokenizing","strategy","for","Reflection","prompts","."]
code: def tokenize_prompt(self, prompt):
      # pylint: disable=duplicate-code
      (
          instruction,
          input,  # pylint: disable=redefined-builtin
          output,
          reflection,
          corrected,
      ) = self.parse_instruction_fields(prompt)
      full_prompt = self._build_full_prompt(
          instruction, input, output, reflection, corrected
      )
      tokenized_full_prompt = self._tokenize(full_prompt)
      if not self.train_on_inputs:
          user_prompt = next(
              iter(
                  self.prompter.build_prompt(
                      instruction,
                      input,
                  )
              )
          )
          tokenized_user_prompt = self._tokenize(
              user_prompt, add_eos_token=False
          )
          user_prompt_len = len(tokenized_user_prompt["input_ids"])
          # TODO this could be sped up using numpy array slicing
          tokenized_full_prompt["labels"] = [
              IGNORE_INDEX
          ] * user_prompt_len + tokenized_full_prompt["labels"][
              user_prompt_len:
          ]
  
      return tokenized_full_prompt
  
imports: import abc
  import copy
  import logging
  from typing import Dict, List, Tuple, Union
  from fastchat.conversation import Conversation
  from transformers import BatchEncoding, PreTrainedTokenizer
  from axolotl.monkeypatch.fastchat_conversation_turns import add_get_turns_to_conversation
  from axolotl.prompters import IGNORE_TOKEN_ID
label_desc: Use image node_id 2 for calling the ReflectionPromptTokenizingStrategy obj's underlying member method code with example usage: obj.tokenize_prompt(prompt) and returns: tokenized_full_prompt