code: def check_llm_finetuning_trainer_config(
      config: "ModelConfig",
  ):  # noqa: F821
      """Ensures that trainer type is finetune if adapter is not None."""
      if config.model_type != MODEL_LLM:
          return
  
      if (
          config.trainer.type == "none"
          and config.adapter is not None
          and config.adapter.pretrained_adapter_weights is not None
      ):
          # If performing zero-shot, we must specify pretrained adapter weights
          return
  
      if (
          config.adapter is not None
          and config.trainer.type != "finetune"
      ):
          raise ConfigValidationError(
              "LLM finetuning requires trainer type to be finetune."
          )
  
imports: from abc import ABC, abstractmethod
  from re import findall
  from typing import Callable, TYPE_CHECKING
  from transformers import AutoConfig
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import AUDIO, BINARY, CATEGORY, IMAGE, IN_MEMORY, MIN_QUANTIZATION_BITS_FOR_MERGE_AND_UNLOAD, MODEL_ECD, MODEL_GBM, MODEL_LLM, NUMBER, SEQUENCE, SET, TEXT, TIMESERIES, VECTOR
  from ludwig.error import ConfigValidationError
  from ludwig.utils.metric_utils import get_feature_to_metric_names_map_from_feature_collection
  from ludwig.utils.misc_utils import merge_dict
label_desc: Use image node_id 26 for calling a global function with example usage: check_llm_finetuning_trainer_config(config) without return types