Losing index information when using dask.dataframe.to_parquet() with partitioning

When I was using dask=1.2.2 with pyarrow 0.11.1 I did not observe this behavior. After updating (dask=2.10.1 and pyarrow=0.15.1), I cannot save the index when I use to_parquet method with given partition_on and write_index arguments. Here I have created a minimal example which shows the issue:

from datetime import timedelta
from pathlib import Path

import dask.dataframe as dd
import pandas as pd

REPORT_DATE_TEST = pd.to_datetime('2019-01-01').date()
path = Path('/home/ludwik/Documents/YieldPlanet/research/trials/')

observations_nr = 3
dtas = range(0, observations_nr)
rds = [REPORT_DATE_TEST - timedelta(days=days) for days in dtas]
data_to_export = pd.DataFrame({
    'report_date': rds,
    'dta': dtas,
    'stay_date': [REPORT_DATE_TEST] * observations_nr,
    }) 
    .set_index('dta')

data_to_export_dask = dd.from_pandas(data_to_export, npartitions=1)

file_name = 'trial.parquet'
data_to_export_dask.to_parquet(path / file_name,
                               engine='pyarrow',
                               compression='snappy',
                               partition_on=['report_date'],
                               write_index=True
                              )

data_read = dd.read_parquet(path / file_name, engine='pyarrow')
print(data_read)

Which gives:

| | stay_date  |dta| report_date|
|0| 2019-01-01 | 2 | 2018-12-30 |
|0| 2019-01-01 | 1 | 2018-12-31 |
|0| 2019-01-01 | 0 | 2019-01-01 |

I did not see that described anywhere in the dask documentation.

Does anyone know how to save the index while partitioning the parquet data?