class_docstr: Abstract class for tokenizing strategies
class_docstr_tok: ["Abstract","class","for","tokenizing","strategies"]
code: def __init__(
      self,
      prompter,
      tokenizer,
      train_on_inputs: bool = False,
      sequence_len: int = 2048,
  ):
      self.prompter = prompter
      self.tokenizer: PreTrainedTokenizer = tokenizer
      self.train_on_inputs = train_on_inputs
      # sequence_len and max_length can be different for CompletionPromptTokenizingStrategy.
      # TODO: Document how they are different.
      self.sequence_len = sequence_len
      self.max_length = sequence_len
  
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 1 to create a new PromptTokenizingStrategy object from inherited base classes: abc with example: obj = PromptTokenizingStrategy(prompter, tokenizer, train_on_inputs, sequence_len)