code: def train_with_backend(
      backend,
      config,
      dataset=None,
      training_set=None,
      validation_set=None,
      test_set=None,
      predict=True,
      evaluate=True,
  ):
      model = LudwigModel(config, backend=backend)
      output_dir = None
  
      ret = False
      try:
          _, _, output_dir = model.train(
              dataset=dataset,
              training_set=training_set,
              validation_set=validation_set,
              test_set=test_set,
              skip_save_processed_input=True,
              skip_save_progress=True,
              skip_save_unprocessed_output=True,
          )
  
          if dataset is None:
              dataset = training_set
  
          if predict:
              preds, _ = model.predict(dataset=dataset)
              assert backend.df_engine.compute(preds) is not None
  
          if evaluate:
              _, eval_preds, _ = model.evaluate(dataset=dataset)
              assert backend.df_engine.compute(eval_preds) is not None
  
          ret = True
      finally:
          # Remove results/intermediate data saved to disk
          shutil.rmtree(output_dir, ignore_errors=True)
      return ret
  
imports: import multiprocessing
  import os
  import random
  import shutil
  import sys
  import traceback
  import unittest
  import uuid
  from distutils.util import strtobool
  import cloudpickle
  import numpy
  import pandas
  from ludwig.api import LudwigModel
  from ludwig.backend import LocalBackend
  from ludwig.constants import VECTOR, COLUMN, NAME, PROC_COLUMN
  from ludwig.data.dataset_synthesizer import DATETIME_FORMATS
  from ludwig.data.dataset_synthesizer import build_synthetic_dataset
  from ludwig.experiment import experiment_cli
  from ludwig.features.feature_utils import compute_feature_hash
  from ludwig.utils.data_utils import read_csv, replace_file_extension
label_desc: Use image node_id 24 for calling a global function with example usage: train_with_backend(backend, config, dataset, training_set, validation_set, test_set, predict, evaluate) and returns: ret