code: def test_ray_preprocessing_placement_group(ray_cluster_2cpu):
      preprocessing_params = {
          "audio_file_length_limit_in_s": 3.0,
          "missing_value_strategy": BFILL,
          "in_memory": True,
          "padding_value": 0,
          "norm": "per_file",
          "type": "fbank",
          "window_length_in_s": 0.04,
          "window_shift_in_s": 0.02,
          "num_filter_bands": 80,
      }
  
      with tempfile.TemporaryDirectory() as tmpdir:
          audio_dest_folder = os.path.join(tmpdir, "generated_audio")
          input_features = [
              audio_feature(
                  folder=audio_dest_folder,
                  preprocessing=preprocessing_params,
              )
          ]
          output_features = [
              binary_feature(),
          ]
  
          config = {
              "input_features": input_features,
              "output_features": output_features,
              TRAINER: {"epochs": 2, "batch_size": 8},
          }
  
          backend_config = {**RAY_BACKEND_CONFIG}
          backend_config["preprocessor_kwargs"] = {"num_cpu": 1}
          csv_filename = os.path.join(tmpdir, "dataset.csv")
          dataset_csv = generate_data(
              input_features,
              output_features,
              csv_filename,
              num_examples=100,
          )
          dataset = create_data_set_to_use(
              "csv", dataset_csv, nan_percent=0.0
          )
          model = LudwigModel(config, backend=backend_config)
          _, _, output_dir = model.train(
              dataset=dataset,
              training_set=dataset,
              skip_save_processed_input=True,
              skip_save_progress=True,
              skip_save_unprocessed_output=True,
              skip_save_log=True,
          )
          preds, _ = model.predict(dataset=dataset)
  
imports: import copy
  import os
  import tempfile
  from typing import Literal, Optional, Union
  import numpy
  import pandas
  import pytest
  import torch
  from ludwig.api import LudwigModel
  from ludwig.backend import create_ray_backend, initialize_backend, LOCAL_BACKEND
  from ludwig.constants import AUDIO, BAG, BALANCE_PERCENTAGE_TOLERANCE, BATCH_SIZE, BFILL, BINARY, CATEGORY, COLUMN, DATE, H3, IMAGE, MAX_BATCH_SIZE_DATASET_FRACTION, NAME, NUMBER, PREPROCESSING, SEQUENCE, SET, SPLIT, TEXT, TIMESERIES, TRAINER, VECTOR
  from ludwig.data.preprocessing import balance_data
  from ludwig.data.split import DEFAULT_PROBABILITIES
  from ludwig.utils.data_utils import read_parquet
  from ludwig.utils.misc_utils import merge_dict
  from tests.integration_tests.utils import audio_feature, augment_dataset_with_none, bag_feature, binary_feature, category_feature, create_data_set_to_use, date_feature, generate_data, h3_feature, image_feature, number_feature, RAY_BACKEND_CONFIG, sequence_feature, set_feature, text_feature, timeseries_feature, train_with_backend, vector_feature
  import dask
  import ray
  import ray.exceptions
  from ray.air.config import DatasetConfig
  from ray.data import Dataset, DatasetPipeline
  from ray.train._internal.dataset_spec import DataParallelIngestSpec
  from ludwig.backend.ray import get_trainer_kwargs, RayBackend
  from ludwig.data.dataframe.dask import DaskEngine
label_desc: Use image node_id 29 for calling a global function with example usage: test_ray_preprocessing_placement_group(ray_cluster_2cpu) without return types