code: def test_from_ray_dataset_empty(tmpdir, ray_cluster_2cpu):
      import dask.dataframe as dd
  
      # Verifies that when the dataset is an empty MapBatches(BatchInferModel), we mitigate Ray's native to_dask()
      # IndexError.
      config = {
          "input_features": [
              {"name": "cat1", "type": "category", "vocab_size": 2},
              {"name": "num1", "type": "number"},
          ],
          "output_features": [
              {"name": "bin1", "type": "binary"},
          ],
          "trainer": {"epochs": 1},
      }
      train_input_df = generate_data_as_dataframe(
          config["input_features"], config["output_features"]
      )
      model = LudwigModel(config, backend="ray")
      model.train(
          train_input_df,
          output_directory=tmpdir,
          skip_save_model=True,
          skip_save_progress=True,
          skip_save_processed_output=True,
          skip_save_processed_input=True,
      )
  
      predict_input_df = dd.from_pandas(
          pd.DataFrame([], columns=["cat1", "num1", "bin1"]),
          npartitions=1,
      )
      model.predict(predict_input_df)
  
imports: import pandas
  import pytest
  from ludwig.api import LudwigModel
  from tests.integration_tests.utils import generate_data_as_dataframe
label_desc: Use image node_id 1 for calling a global function with example usage: test_from_ray_dataset_empty(tmpdir, ray_cluster_2cpu) without return types