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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 predictions(self, setup, n_jobs=-1):
 """ Returns a `pandas.DataFrame`_ with the weighted average predictions and variance of all readouts for each possible clampings in the given experimental setup. For each logical network the weight corresponds to the number of networks having the same behavior. Parameters setup : :class:`caspo.core.setup.Setup` Experimental setup n_jobs : int Number of jobs to run in parallel. Default to -1 (all cores available) Returns ------- `pandas.DataFrame`_ DataFrame with the weighted average predictions and variance of all readouts for each possible clamping .. _pandas.DataFrame: http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dataframe .. seealso:: `Wikipedia: Weighted sample variance <https://en.wikipedia.org/wiki/Weighted_arithmetic_mean#Weighted_sample_variance>`_ """

        stimuli, inhibitors, readouts = setup.stimuli, setup.inhibitors, setup.readouts
        nc = len(setup.cues())
        predictions = np.zeros((len(self), 2**nc, len(setup)))
        predictions[:, :, :] = Parallel(n_jobs=n_jobs)(delayed(__parallel_predictions__)(n, list(setup.clampings_iter(setup.cues())), readouts, stimuli, inhibitors) for n in self)

        avg = np.average(predictions[:, :, nc:], axis=0, weights=self.__networks)
        var = np.average((predictions[:, :, nc:]-avg)**2, axis=0, weights=self.__networks)

        rcues = ["TR:%s" % c for c in setup.cues(True)]
        cols = np.concatenate([rcues, ["AVG:%s" % r for r in readouts], ["VAR:%s" % r for r in readouts]])

        #use the first network predictions to extract all clampings
        df = pd.DataFrame(np.concatenate([predictions[0, :, :nc], avg, var], axis=1), columns=cols)
        df[rcues] = df[rcues].astype(int)

        return df