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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 linear_trend_timewise(x, param):
 """ Calculate a linear least-squares regression for the values of the time series versus the sequence from 0 to length of the time series minus one. This feature uses the index of the time series to fit the model, which must be of a datetime dtype. The parameters control which of the characteristics are returned. Possible extracted attributes are "pvalue", "rvalue", "intercept", "slope", "stderr", see the documentation of linregress for more information. :param x: the time series to calculate the feature of. The index must be datetime. :type x: pandas.Series :param param: contains dictionaries {"attr": x} with x an string, the attribute name of the regression model :type param: list :return: the different feature values :return type: list """

    ix = x.index

    # Get differences between each timestamp and the first timestamp in seconds.
    # Then convert to hours and reshape for linear regression
    times_seconds = (ix - ix[0]).total_seconds()
    times_hours = np.asarray(times_seconds / float(3600))

    linReg = linregress(times_hours, x.values)

    return [("attr_\"{}\"".format(config["attr"]), getattr(linReg, config["attr"]))
            for config in param]