code: def check(self) -> Dict[str, List]:
      errors: Dict[str, List] = {"title": [], "message": []}
      if (
          self.prediction.temperature > 0
          and not self.prediction.do_sample
      ):
          errors["title"] += [
              "Do sample needs to be enabled for temperature > 0"
          ]
          errors["message"] += [
              "Please enable do sample if you want to use temperature > 0."
          ]
      return errors
  
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 2 for calling the ConfigProblemBase obj's underlying member method code with example usage: obj.check() and returns: errors