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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 compute_exposures(positions, factor_loadings):
 """ Compute daily risk factor exposures. Parameters positions: pd.Series A series of holdings as percentages indexed by date and ticker. - Examples: dt ticker 2017-01-01 AAPL 0.417582 TLT 0.010989 XOM 0.571429 2017-01-02 AAPL 0.202381 TLT 0.535714 XOM 0.261905 factor_loadings : pd.DataFrame Factor loadings for all days in the date range, with date and ticker as index, and factors as columns. - Example: momentum reversal dt ticker 2017-01-01 AAPL -1.592914 0.852830 TLT 0.184864 0.895534 XOM 0.993160 1.149353 2017-01-02 AAPL -0.140009 -0.524952 TLT -1.066978 0.185435 XOM -1.798401 0.761549 Returns ------- risk_exposures_portfolio : pd.DataFrame df indexed by datetime, with factors as columns - Example: momentum reversal dt 2017-01-01 -0.238655 0.077123 2017-01-02 0.821872 1.520515 """

    risk_exposures = factor_loadings.multiply(positions, axis='rows')
    return risk_exposures.groupby(level='dt').sum()