code: def __post_init__(self):
      super().__post_init__()
      self.lora = True
      self._possible_values[
          "differential_learning_rate_layers"
      ] = possible_values.String(
          values=("backbone", "value_head"),
          allow_custom=False,
          placeholder="Select optional layers...",
      )
      self._possible_values["grad_accumulation"] = (1, 128, 1)
  
      self._possible_values["rollout_steps"] = (1, 1024, 1)
      self._possible_values["initial_kl_coefficient"] = (
          0.01,
          0.5,
          0.01,
      )
      self._possible_values["kl_target"] = (0.1, 16, 0.1)
      self._possible_values["kl_horizon"] = (1000, 20000, 1000)
      self._possible_values["advantages_gamma"] = (0.800, 0.999, 0.001)
      self._possible_values["advantages_lambda"] = (0.8, 1.0, 0.01)
      self._possible_values["ppo_clip_policy"] = (0.1, 0.5, 0.05)
      self._possible_values["ppo_clip_value"] = (0.1, 0.5, 0.05)
      self._possible_values["scaling_factor_value_loss"] = (
          0.01,
          1,
          0.01,
      )
      self._possible_values["ppo_epochs"] = (1, 16, 1)
      self._possible_values["ppo_generate_temperature"] = (
          0.1,
          1.0,
          0.1,
      )
      self._possible_values["ppo_batch_size"] = (1, 256, 1)
  
      self._order.insert(
          "rollout_steps",
          "offload_reward_model",
          "adaptive_kl_control",
          "full_kl_penalty",
          "advantages_gamma",
          "kl_horizon",
          "ppo_generate_temperature",
          "kl_target",
          "scaling_factor_value_loss",
          "ppo_clip_value",
          "ppo_clip_policy",
          "initial_kl_coefficient",
          "advantages_lambda",
          "ppo_batch_size",
          "ppo_epochs",
          after="learning_rate",
      )
  
      self._visibility["lora"] = -1
      self._visibility["loss_function"] = -1
  
imports: import os
  from dataclasses import dataclass, field
  from typing import Any, Dict, List
  import llm_studio.src.models.text_rlhf_language_modeling_model
  from llm_studio.python_configs.base import DefaultConfigProblemBase
  from llm_studio.python_configs.text_causal_language_modeling_config import ConfigNLPAugmentation, ConfigNLPCausalLMArchitecture, ConfigNLPCausalLMDataset, ConfigNLPCausalLMEnvironment, ConfigNLPCausalLMLogging, ConfigNLPCausalLMPrediction, ConfigNLPCausalLMTokenizer, ConfigNLPCausalLMTraining
  from llm_studio.src import possible_values
  from llm_studio.src.datasets.text_rlhf_modeling_ds import CustomDataset
  from llm_studio.src.models import text_reward_model
  from llm_studio.src.utils.modeling_utils import generate_experiment_name
label_desc: Use image node_id 1 for calling the ConfigRLHFLMTraining obj's underlying member method code with example usage: obj.__post_init__() without return types