code: def _prepare_finetuning_test(
      csv_filename: str,
      finetune_strategy: str,
      backend: dict,
      adapter_args: dict,
  ) -> tuple[dict, str]:
      input_features = [
          text_feature(name="input", encoder={"type": "passthrough"})
      ]
      output_features = [text_feature(name="output")]
  
      train_df = generate_data(
          input_features,
          output_features,
          filename=csv_filename,
          num_examples=25,
      )
      prediction_df = pd.DataFrame(
          [
              {"input": "The food was amazing!", "output": "positive"},
              {
                  "input": "The service was terrible.",
                  "output": "negative",
              },
              {"input": "The food was okay.", "output": "neutral"},
          ]
      )
  
      model_name = TEST_MODEL_NAME
      if finetune_strategy == "adalora":
          # Adalora isn't supported for GPT-J model types, so use tiny bart
          model_name = "hf-internal-testing/tiny-random-BartModel"
      elif finetune_strategy == "adaption_prompt":
          # At the time of writing this test, Adaption Prompt fine-tuning is only supported for Llama models
          model_name = "yujiepan/llama-2-tiny-random"
  
      config = {
          MODEL_TYPE: MODEL_LLM,
          BASE_MODEL: model_name,
          INPUT_FEATURES: input_features,
          OUTPUT_FEATURES: output_features,
          TRAINER: {
              TYPE: "finetune",
              BATCH_SIZE: 8,
              EPOCHS: 2,
          },
          BACKEND: backend,
      }
  
      if finetune_strategy is not None:
          config[ADAPTER] = {
              TYPE: finetune_strategy,
              **adapter_args,
          }
  
      return train_df, prediction_df, config
  
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 10 for calling a global function with example usage: _prepare_finetuning_test(csv_filename, finetune_strategy, backend, adapter_args) and returns: train_df,  prediction_df,  config