code: def __post_init__(self):
      super().__post_init__()
      self._possible_values["beta"] = possible_values.Number(
          0.05, 0.5, 0.05
      )
      self._order.insert("beta", after="learning_rate")
      self._nesting.add(
          ["beta"],
          dependencies=[
              Dependency(
                  key="loss_function", value="DPOLoss", is_set=True
              )
          ],
      )
      self._visibility["lora"] = -1
  
imports: import os
  from dataclasses import dataclass, field
  from typing import Any
  import llm_studio.src.datasets.text_dpo_modeling_ds
  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.losses import text_dpo_modeling_losses
  from llm_studio.src.models import text_dpo_modeling_model
  from llm_studio.src.nesting import Dependency
  from llm_studio.src.plots import text_dpo_modeling_plots
  from llm_studio.src.utils.modeling_utils import generate_experiment_name
label_desc: Use image node_id 1 for calling the ConfigDPOTraining obj's underlying member method code with example usage: obj.__post_init__() without return types