code: def __post_init__(self):
      super().__post_init__()
      self._possible_values["max_length_prompt"] = (32, 1024 * 16, 32)
      self._possible_values["max_length_answer"] = (32, 1024 * 16, 32)
      self._possible_values["max_length"] = (32, 1024 * 16, 32)
      self._possible_values["padding_quantile"] = (0, 1, 0.01)
      self._padding_side = "left"
  
      self._visibility["add_prefix_space"] = -1
  
imports: import multiprocessing
  import os
  from dataclasses import dataclass, field
  from typing import Any, Dict, List, Tuple
  import torch
  import llm_studio.src.datasets.text_causal_language_modeling_ds
  from llm_studio.python_configs.base import DefaultConfig, DefaultConfigProblemBase
  from llm_studio.src import possible_values
  from llm_studio.src.augmentations.nlp_aug import BaseNLPAug
  from llm_studio.src.loggers import Loggers
  from llm_studio.src.losses import text_causal_language_modeling_losses
  from llm_studio.src.metrics import text_causal_language_modeling_metrics
  from llm_studio.src.models import text_causal_language_modeling_model
  from llm_studio.src.nesting import Dependency
  from llm_studio.src.optimizers import Optimizers
  from llm_studio.src.plots import text_causal_language_modeling_plots
  from llm_studio.src.schedulers import Schedulers
  from llm_studio.src.utils.modeling_utils import generate_experiment_name
label_desc: Use image node_id 1 for calling the ConfigNLPCausalLMTokenizer obj's underlying member method code with example usage: obj.__post_init__() without return types