code: def test_write_dataframe_to_ts_success(tmp_path, dataset):
      """Tests whether a dataset can be written by the .ts writer then read in."""
      # load an example dataset
      path = os.path.join(
          os.path.dirname(sktime.__file__),
          f"datasets/data/{dataset}/{dataset}_TEST.ts",
      )
      test_X, test_y = load_from_tsfile_to_dataframe(path)
      # output the dataframe in a ts file
      write_dataframe_to_tsfile(
          data=test_X,
          path=tmp_path,
          problem_name=dataset,
          class_label=np.unique(test_y),
          class_value_list=test_y,
          equal_length=True,
          comment="""
            The data was derived from twelve monthly electrical power demand
            time series from Italy and first used in the paper "Intelligent
            Icons: Integrating Lite-Weight Data Mining and Visualization into
            GUI Operating Systems". The classification task is to distinguish
            days from Oct to March (inclusive) from April to September.
          """,
          fold="_transform",
      )
      # load data back from the ts file
      result = f"{tmp_path}/{dataset}/{dataset}_transform.ts"
      res_X, res_y = load_from_tsfile_to_dataframe(result)
      # check if the dataframes are the same
      assert_frame_equal(res_X, test_X)
  
imports: import os
  import tempfile
  import numpy
  import pandas
  import pytest
  from pandas._testing import assert_frame_equal
  import sktime
  from sktime.datasets import generate_example_long_table, load_from_long_to_dataframe, load_from_tsfile, load_from_tsfile_to_dataframe, load_tsf_to_dataframe, write_dataframe_to_tsfile, write_panel_to_tsfile
  from sktime.datasets._data_io import MODULE, _load_provided_dataset
  from sktime.datasets._readers_writers.tsf import _convert_tsf_to_hierarchical
  from sktime.datatypes import check_is_mtype
label_desc: Use image node_id 9 for calling a global function with example usage: test_write_dataframe_to_ts_success(tmp_path, dataset) without return types