code: def test_api_skip_parameters_evaluate(
      tmpdir,
      csv_filename,
      skip_save_unprocessed_output,
      skip_save_predictions,
      skip_save_eval_stats,
      skip_collect_predictions,
      skip_collect_overall_stats,
  ):
      # Single sequence input, single category output
      input_features = [category_feature(encoder={"vocab_size": 5})]
      output_features = [category_feature(decoder={"vocab_size": 5})]
  
      # Generate test data
      rel_path = generate_data(
          input_features,
          output_features,
          os.path.join(tmpdir, csv_filename),
      )
      run_api_commands(
          input_features,
          output_features,
          data_csv=rel_path,
          output_dir=tmpdir,
          skip_save_unprocessed_output=skip_save_unprocessed_output,
          skip_save_predictions=skip_save_predictions,
          skip_save_eval_stats=skip_save_eval_stats,
          skip_collect_predictions=skip_collect_predictions,
          skip_collect_overall_stats=skip_collect_overall_stats,
      )
  
imports: import json
  import os
  import shutil
  from unittest import mock
  import pandas
  import pytest
  import torch
  import yaml
  from ludwig.api import LudwigModel
  from ludwig.callbacks import Callback
  from ludwig.constants import BATCH_SIZE, ENCODER, TRAINER, TYPE
  from ludwig.globals import MODEL_HYPERPARAMETERS_FILE_NAME
  from ludwig.models.inference import InferenceModule
  from ludwig.utils.data_utils import read_csv
  from tests.integration_tests.utils import category_feature, ENCODERS, generate_data, get_weights, image_feature, run_api_experiment, sequence_feature, text_feature
label_desc: Use image node_id 10 for calling a global function with example usage: test_api_skip_parameters_evaluate(tmpdir, csv_filename, skip_save_unprocessed_output, skip_save_predictions, skip_save_eval_stats, skip_collect_predictions, skip_collect_overall_stats) without return types