code: def __post_init__(self):
      super().__post_init__()
      self._possible_values["token_mask_probability"] = (0.0, 0.9, 0.05)
      self._possible_values["skip_parent_probability"] = (
          0.0,
          1.0,
          0.05,
      )
      self._possible_values["random_parent_probability"] = (
          0.0,
          1.0,
          0.05,
      )
      self._possible_values["neftune_noise_alpha"] = (0.0, 15, 0.05)
      self._visibility["nlp_augmentations_class"] = -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 ConfigNLPAugmentation obj's underlying member method code with example usage: obj.__post_init__() without return types