code: def check(self) -> Dict[str, List]:
      errors: Dict[str, List] = {"title": [], "message": []}
      if not self.training.lora:
          errors["title"] += ["LoRA must be True for RLHF"]
          errors["message"] += [
              "LoRA must be True for RLHF. "
              "Please set LoRA to True or change the problem type. "
          ]
  
      # see CustomDataset for RLHF
      if self.dataset.system_column != "None":
          errors["title"] += [
              "RLHF is not compatible with system column."
          ]
          errors["message"] += [
              "RLHF is not compatible with system column. "
              "Please set system column to None or change the problem type. "
          ]
      if self.dataset.limit_chained_samples:
          errors["title"] += [
              "RLHF is not compatible with limit_chained_samples."
          ]
          errors["message"] += [
              "RLHF is not compatible with limit_chained_samples. "
              "Please set limit_chained_samples to False or change the problem type. "
          ]
      if not self.dataset.mask_prompt_labels:
          errors["title"] += [
              "RLHF is not compatible with mask_prompt_labels."
          ]
          errors["message"] += [
              "RLHF is not compatible with mask_prompt_labels. "
              "Please set mask_prompt_labels to True or change the problem type. "
          ]
      return errors
  
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 2 for calling the ConfigProblemBase obj's underlying member method code with example usage: obj.check() and returns: errors