code: def test_ray_read_binary_files(tmpdir, df_engine, 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",
          "audio_feature": {
              "type": "fbank",
              "window_length_in_s": 0.04,
              "window_shift_in_s": 0.02,
              "num_filter_bands": 80,
          },
      }
      audio_dest_folder = os.path.join(tmpdir, "generated_audio")
      audio_params = audio_feature(
          folder=audio_dest_folder, preprocessing=preprocessing_params
      )
  
      dataset_path = os.path.join(tmpdir, "dataset.csv")
      dataset_path = generate_data(
          [audio_params], [], dataset_path, num_examples=10
      )
      dataset_path = create_data_set_to_use(
          "csv", dataset_path, nan_percent=0.1
      )
  
      backend_config = {**RAY_BACKEND_CONFIG}
      backend_config["processor"]["type"] = df_engine
      backend = initialize_backend(backend_config)
      df = backend.df_engine.df_lib.read_csv(dataset_path)
      series = df[audio_params[COLUMN]]
      proc_col = backend.read_binary_files(series)
      proc_col = backend.df_engine.compute(proc_col)
  
      backend = initialize_backend(LOCAL_BACKEND)
      df = backend.df_engine.df_lib.read_csv(dataset_path)
      series = df[audio_params[COLUMN]]
      proc_col_expected = backend.read_binary_files(series)
  
      assert proc_col.equals(proc_col_expected)
  
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 9 for calling a global function with example usage: test_ray_read_binary_files(tmpdir, df_engine, ray_cluster_2cpu) without return types