code: def test_sequence_decoder_predictions(
      tmpdir,
      csv_filename,
      ray_cluster_2cpu,
      feature_type,
      feature_gen,
      decoder_type,
  ):
      """Test that sequence decoders return the correct successfully predict."""
      input_feature = feature_gen()
      output_feature = feature_gen(output_feature=True)
  
      input_feature[ENCODER] = {TYPE: "embed", "reduce_output": None}
      output_feature[DECODER] = {TYPE: decoder_type}
  
      dataset_path = generate_data(
          input_features=[input_feature],
          output_features=[output_feature],
          filename=os.path.join(tmpdir, csv_filename),
      )
      dataset_path = create_data_set_to_use("csv", dataset_path)
  
      config = {
          INPUT_FEATURES: [input_feature],
          TRAINER: {EPOCHS: 1, BATCH_SIZE: 4},
      }
  
      # Ensure that the decoder outputs the correct predictions through both the default and feature-specific configs.
      config[OUTPUT_FEATURES] = [output_feature]
  
      # Test with decoder in output feature config
      train_with_backend(
          RAY_BACKEND_CONFIG, config=config, dataset=dataset_path
      )
  
imports: import os
  import pytest
  from ludwig.constants import BATCH_SIZE, DECODER, ENCODER, EPOCHS, INPUT_FEATURES, OUTPUT_FEATURES, SEQUENCE, TEXT, TRAINER, TYPE
  from tests.integration_tests.utils import create_data_set_to_use, generate_data, RAY_BACKEND_CONFIG, sequence_feature, text_feature, train_with_backend
label_desc: Use image node_id 1 for calling a global function with example usage: test_sequence_decoder_predictions(tmpdir, csv_filename, ray_cluster_2cpu, feature_type, feature_gen, decoder_type) without return types