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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|. The documentation on |NetCDFVariableDeep.shape| explains how |NetCDFVariableDeep.array| is structured. The first example confirms that, for the default configuration, the first axis definces the location, while the second one defines time: array([[ 0., 1., 2., 3.], [ 4., 5., 6., 7.], [ 8., 9., 10., 11.]]) For higher dimensional sequences, |NetCDFVariableDeep.array| can contain missing values. Such missing values show up for some fiels of the second example element, which defines only two hydrological response units instead of three: array([[ 16., 17., nan], [ 18., 19., nan], [ 20., 21., nan], [ 22., 23., nan]]) When using the first axis for time (`timeaxis=0`) the same data can be accessed with slightly different indexing: array([[ 16., 17., nan], [ 18., 19., nan], [ 20., 21., nan], [ 22., 23., nan]]) """

        array = numpy.full(self.shape, fillvalue, dtype=float)
        for idx, (descr, subarray) in enumerate(self.arrays.items()):
            sequence = self.sequences[descr]
            array[self.get_slices(idx, sequence.shape)] = subarray
        return array