code: def test_llm_quantization_config(
      bits: Optional[int],
      expected_qconfig: Optional[QuantizationConfig],
  ):
      config = {
          MODEL_TYPE: MODEL_LLM,
          BASE_MODEL: "bigscience/bloomz-3b",
          "quantization": {"bits": bits},
          INPUT_FEATURES: [{NAME: "text_input", TYPE: "text"}],
          OUTPUT_FEATURES: [{NAME: "text_output", TYPE: "text"}],
      }
  
      if bits is None:
          del config["quantization"]
  
      config_obj = ModelConfig.from_dict(config)
  
      assert config_obj.quantization == expected_qconfig
  
imports: import os
  from tempfile import TemporaryDirectory
  from typing import Any, Dict, Optional, Union
  import pytest
  import yaml
  from ludwig.constants import ACTIVE, BASE_MODEL, CLIP, COLUMN, COMBINER, DECODER, DEFAULT_VALIDATION_METRIC, DEFAULTS, DEPENDENCIES, ENCODER, HYPEROPT, INPUT_FEATURES, INPUT_SIZE, LOSS, MODEL_ECD, MODEL_GBM, MODEL_LLM, MODEL_TYPE, NAME, NUM_CLASSES, OPTIMIZER, OUTPUT_FEATURES, PREPROCESSING, PROC_COLUMN, REDUCE_DEPENDENCIES, REDUCE_INPUT, TIED, TRAINER, TYPE
  from ludwig.error import ConfigValidationError
  from ludwig.schema.decoders.base import ClassifierConfig
  from ludwig.schema.encoders.text_encoders import BERTConfig
  from ludwig.schema.features.augmentation.image import RandomBlurConfig, RandomRotateConfig
  from ludwig.schema.features.image_feature import AUGMENTATION_DEFAULT_OPERATIONS
  from ludwig.schema.features.number_feature import NumberOutputFeatureConfig
  from ludwig.schema.features.text_feature import TextOutputFeatureConfig
  from ludwig.schema.llms.quantization import QuantizationConfig
  from ludwig.schema.model_config import ModelConfig
  from ludwig.schema.utils import BaseMarshmallowConfig, convert_submodules
label_desc: Use image node_id 26 for calling a global function with example usage: test_llm_quantization_config(bits, expected_qconfig) without return types