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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 sliding_window(time_series, width, step, order='F'):
    '''
    Segments univariate time series with sliding window

    Parameters
    ----------
    time_series : array like shape [n_samples]
        time series or sequence
    width : int > 0
        segment width in samples
    step : int > 0
        stepsize for sliding in samples

    Returns
    -------
    w : array like shape [n_segments, width]
        resampled time series segments
    '''
    w = np.hstack([time_series[i:1 + i - width or None:step] for i in range(0, width)])
    result = w.reshape((int(len(w) / width), width), order='F')
    if order == 'F':
        return result
    else:
        return np.ascontiguousarray(result)