code: def __post_init__(self):
      super().__post_init__()
  
      self._possible_values["backbone_dtype"] = possible_values.String(
          values=("float32", "bfloat16", "float16", "int8", "int4"),
          allow_custom=False,
      )
      self._possible_values["intermediate_dropout"] = (0, 0.5, 0.05)
  
      self._nesting.add(
          ["force_embedding_gradients"],
          [Dependency(key="lora", value=False, is_set=False)],
      )
  
      self._visibility["model_class"] = -1
      self._visibility["pretrained"] = -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 ConfigNLPCausalLMArchitecture obj's underlying member method code with example usage: obj.__post_init__() without return types