code: def predict_with_backend(
      tmpdir, config, data_csv_path, backend, patch_args=None
  ):
      if backend == "ray":
          backend = RAY_BACKEND_CONFIG
          backend["processor"]["type"] = "dask"
  
      ludwig_model = LudwigModel(config, backend=backend)
      _, _, output_directory = ludwig_model.train(
          dataset=data_csv_path,
          output_directory=os.path.join(tmpdir, "output"),
      )
      # Check that metadata JSON saves and loads correctly
      ludwig_model = LudwigModel.load(
          os.path.join(output_directory, "model")
      )
  
      if patch_args is not None:
          with mock.patch(*patch_args):
              preds_df, _ = ludwig_model.predict(dataset=data_csv_path)
      else:
          preds_df, _ = ludwig_model.predict(dataset=data_csv_path)
  
      return preds_df, ludwig_model
  
file_path: ludwig/tests/integration_tests/test_postprocessing.py
imports: import os
  from functools import partial
  from unittest import mock
  import numpy
  import pandas
  import pytest
  import torch
  from ludwig.api import LudwigModel
  from ludwig.constants import BATCH_SIZE, DECODER, NAME, TRAINER
  from tests.integration_tests.utils import binary_feature, category_feature, generate_data, RAY_BACKEND_CONFIG, set_feature, text_feature
label_desc: Use image node_id 6 for calling a global function with example usage: predict_with_backend(tmpdir, config, data_csv_path, backend, patch_args) and returns: preds_df,  ludwig_model