code: def test_llm_lora_finetuning_merge_and_unload_4_bit_quantization_not_supported(
      local_backend: dict,
  ):
      input_features: list[dict] = [
          text_feature(name="input", encoder={"type": "passthrough"})
      ]
      output_features: list[dict] = [text_feature(name="output")]
      finetune_strategy: str = "lora"
  
      config: dict = {
          MODEL_TYPE: MODEL_LLM,
          BASE_MODEL: TEST_MODEL_NAME,
          INPUT_FEATURES: input_features,
          OUTPUT_FEATURES: output_features,
          TRAINER: {
              TYPE: "finetune",
              BATCH_SIZE: 8,
              EPOCHS: 2,
          },
          ADAPTER: {
              TYPE: finetune_strategy,
              POSTPROCESSOR: {
                  MERGE_ADAPTER_INTO_BASE_MODEL: True,
                  PROGRESSBAR: True,
              },
          },
          QUANTIZATION: {"bits": 4},
          BACKEND: local_backend,
      }
  
      expected_error_class: type = ludwig_error.ConfigValidationError
      expected_error_message: str = """This operation will entail merging LoRA layers on a 4-bit quantized model.  \
  Calling "save_pretrained()" on that model is currently unsupported.  If you want to merge the LoRA adapter weights \
  into the base model, you need to use 8-bit quantization or do non-quantized based training by removing the \
  quantization section from your Ludwig configuration."""
      with pytest.raises(expected_error_class) as excinfo:
          _ = LudwigModel(config)
  
      assert str(excinfo.value) == expected_error_message
  
imports: from __future__ import annotations
  import copy
  import json
  import os
  import pathlib
  from typing import Any
  import numpy
  import pandas
  import pytest
  import torch
  import ludwig.error
  from ludwig.api import LudwigModel
  from ludwig.constants import ADAPTER, BACKEND, BASE_MODEL, BATCH_SIZE, COMBINER, EPOCHS, GENERATION, INPUT_FEATURES, MERGE_ADAPTER_INTO_BASE_MODEL, MODEL_ECD, MODEL_LLM, MODEL_TYPE, OUTPUT_FEATURES, POSTPROCESSOR, PREPROCESSING, PRETRAINED_ADAPTER_WEIGHTS, PROGRESSBAR, PROMPT, QUANTIZATION, TARGET_MODULES, TRAINER, TYPE
  from ludwig.models.llm import LLM
  from ludwig.schema.model_types.base import ModelConfig
  from ludwig.utils.fs_utils import list_file_names_in_directory
  from ludwig.utils.types import DataFrame
  from tests.integration_tests.utils import category_feature, generate_data, text_feature
label_desc: Use image node_id 16 for calling a global function with example usage: test_llm_lora_finetuning_merge_and_unload_4_bit_quantization_not_supported(local_backend) without return types