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 os
  from dataclasses import dataclass, field
  from typing import Any, Dict, List
  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.models import text_sequence_to_sequence_modeling_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