code: def test_llm_few_shot_classification(
      tmpdir, backend, csv_filename, ray_cluster_4cpu
  ):
      input_features = [
          text_feature(
              output_feature=False,
              name="body",
              encoder={
                  "type": "passthrough"
              },  # need to use the default encoder for LLMTextInputFeatureConfig
          )
      ]
      output_features = [
          category_feature(
              output_feature=True,
              name="output",
              preprocessing={
                  "fallback_label": "3",
              },
              decoder={
                  "type": "category_extractor",
                  "match": {
                      "1": {"type": "contains", "value": "1"},
                      "2": {"type": "contains", "value": "2"},
                      "3": {"type": "contains", "value": "3"},
                      "4": {"type": "contains", "value": "4"},
                      "5": {"type": "contains", "value": "5"},
                  },
              },
          )
      ]
  
      config = {
          MODEL_TYPE: MODEL_LLM,
          BASE_MODEL: TEST_MODEL_NAME,
          GENERATION: get_generation_config(),
          PROMPT: {
              "retrieval": {"type": "random", "k": 3},
              "task": (
                  "Given the sample input, complete this sentence by replacing XXXX: The review rating is XXXX. "
                  "Choose one value in this list: [1, 2, 3, 4, 5]."
              ),
          },
          INPUT_FEATURES: input_features,
          OUTPUT_FEATURES: output_features,
          PREPROCESSING: {
              "split": {TYPE: "fixed"},
          },
          BACKEND: {**backend, "cache_dir": str(tmpdir)},
      }
  
      dataset_path = generate_data(
          input_features,
          output_features,
          filename=csv_filename,
          num_examples=25,
          nan_percent=0.1,
          with_split=True,
      )
      df = pd.read_csv(dataset_path)
      df["output"] = np.random.choice(
          [1, 2, 3, 4, 5], size=len(df)
      ).astype(
          str
      )  # ensure labels match the feature config
      df.to_csv(dataset_path, index=False)
  
      model = LudwigModel(config)
      model.train(
          dataset=dataset_path,
          output_directory=str(tmpdir),
          skip_save_processed_input=True,
      )
  
      # TODO: fix LLM model loading
      # model = LudwigModel.load(os.path.join(results.output_directory, "model"), backend=backend)
      preds, _ = model.predict(dataset=dataset_path)
      preds = convert_preds(preds)
  
      assert preds
  
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 9 for calling a global function with example usage: test_llm_few_shot_classification(tmpdir, backend, csv_filename, ray_cluster_4cpu) without return types