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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 array(self) -> numpy.ndarray: """The series data of all logged |IOSequence| objects contained in one single |numpy.ndarray| object. The documentation on |NetCDFVariableAgg.shape| explains how |NetCDFVariableAgg.array| is structured. The first example confirms that, under default configuration (`timeaxis=1`), the first axis corresponds to the location, while the second one corresponds to time: array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.]]) Due to the flattening of higher dimensional sequences, their individual time series (e.g. of different hydrological response units) are spread over the rows of the array. For the 1-dimensional sequence |lland_fluxes.NKor|, the individual time series of the second element are stored in row two and three: array([[ 16., 18., 20., 22.], [ 17., 19., 21., 23.]]) When using the first axis as the "timeaxis", the individual time series of the second element are stored in column two and three: array([[ 16., 17.], [ 18., 19.], [ 20., 21.], [ 22., 23.]]) """

        array = numpy.full(self.shape, fillvalue, dtype=float)
        idx0 = 0
        idxs: List[Any] = [slice(None)]
        for seq, subarray in zip(self.sequences.values(),
                                 self.arrays.values()):
            for prod in self._product(seq.shape):
                subsubarray = subarray[tuple(idxs + list(prod))]
                array[self.get_timeplaceslice(idx0)] = subsubarray
                idx0 += 1
        return array