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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def get_weather_data(filename='weather.csv', **kwargs):
 r""" Imports weather data from a file. The data include wind speed at two different heights in m/s, air temperature in two different heights in K, surface roughness length in m and air pressure in Pa. The file is located in the example folder of the windpowerlib. The height in m for which the data applies is specified in the second row. Parameters filename : string Filename of the weather data file. Default: 'weather.csv'. Other Parameters datapath : string, optional Path where the weather data file is stored. Default: 'windpowerlib/example'. Returns ------- weather_df : pandas.DataFrame DataFrame with time series for wind speed `wind_speed` in m/s, temperature `temperature` in K, roughness length `roughness_length` in m, and pressure `pressure` in Pa. The columns of the DataFrame are a MultiIndex where the first level contains the variable name as string (e.g. 'wind_speed') and the second level contains the height as integer at which it applies (e.g. 10, if it was measured at a height of 10 m). """


    if 'datapath' not in kwargs:
        kwargs['datapath'] = os.path.join(os.path.split(
            os.path.dirname(__file__))[0], 'example')
    file = os.path.join(kwargs['datapath'], filename)
    # read csv file
    weather_df = pd.read_csv(
        file, index_col=0, header=[0, 1],
        date_parser=lambda idx: pd.to_datetime(idx, utc=True))
    # change type of index to datetime and set time zone
    weather_df.index = pd.to_datetime(weather_df.index).tz_convert(
        'Europe/Berlin')
    # change type of height from str to int by resetting columns
    weather_df.columns = [weather_df.axes[1].levels[0][
                              weather_df.axes[1].codes[0]],
                          weather_df.axes[1].levels[1][
                              weather_df.axes[1].codes[1]].astype(int)]
    return weather_df