code: def __post_init__(self):
      super().__post_init__()
      self._possible_values["loss_function"] = self.loss_class.names()
      self._possible_values["optimizer"] = Optimizers.names()
  
      self._possible_values["learning_rate"] = possible_values.Number(
          step=0.000001, min=0.000001
      )
      self._possible_values[
          "differential_learning_rate_layers"
      ] = possible_values.String(
          values=("backbone", "embed"),
          allow_custom=False,
          placeholder="Select optional layers...",
      )
      self._possible_values[
          "differential_learning_rate"
      ] = self._possible_values["learning_rate"]
  
      self._possible_values["batch_size"] = (1, 256, 1)
      self._possible_values["epochs"] = (0, 10, 1)
      self._possible_values["schedule"] = Schedulers.names()
      self._possible_values["warmup_epochs"] = (0.0, 5, 0.05)
  
      self._possible_values["weight_decay"] = possible_values.Number(
          step=1e-5, min=0
      )
      self._possible_values["gradient_clip"] = (0.0, 10.0, 0.1)
      self._possible_values["grad_accumulation"] = (1, 8, 1)
  
      self._possible_values["lora_r"] = (1, 256, 1)
      self._possible_values["lora_alpha"] = (1, 256, 1)
      self._possible_values["lora_dropout"] = (0.0, 0.5, 0.01)
  
      self._possible_values["evaluation_epochs"] = (0.01, 1, 0.01)
  
      self._visibility["loss_class"] = -1
      self._visibility["drop_last_batch"] = -1
      self._visibility["differential_learning_rate_layers"] = 1
      self._visibility["differential_learning_rate"] = 1
  
      self._nesting.add(
          ["differential_learning_rate"],
          [
              Dependency(
                  key="differential_learning_rate_layers",
                  value=None,
                  is_set=False,
              )
          ],
      )
      self._nesting.add(
          [
              "lora_r",
              "lora_alpha",
              "lora_dropout",
              "lora_target_modules",
          ],
          [Dependency(key="lora", value=False, is_set=False)],
      )
      self._nesting.add(
          ["train_validation_data"],
          [
              Dependency(
                  key="save_best_checkpoint", value=False, is_set=True
              )
          ],
      )
  
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 ConfigNLPCausalLMTraining obj's underlying member method code with example usage: obj.__post_init__() without return types