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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 log(self, sequence, infoarray) -> None: """Pass the given |IoSequence| to a suitable instance of a |NetCDFVariableBase| subclass. When writing data, the second argument should be an |InfoArray|. When reading data, this argument is ignored. Simply pass |None|. (1) We prepare some devices handling some sequences by applying function |prepare_io_example_1|. We limit our attention to the returned elements, which handle the more diverse sequences: (2) We define some shortcuts for the sequences used in the following examples: (3) We define a function that logs these example sequences to a given |NetCDFFile| object and prints some information about the resulting object structure. Note that sequence `nkor2` is logged twice, the first time with its original time series data, the second time with averaged values: (4) We prepare a |NetCDFFile| object with both options `flatten` and `isolate` being disabled: (5) We log all test sequences results in two |NetCDFVariableDeep| and one |NetCDFVariableAgg| objects. To keep both NetCDF variables related to |lland_fluxes.NKor| distinguishable, the name `flux_nkor_mean` includes information about the kind of aggregation performed: input_nied NetCDFVariableDeep ('element1', 'element2') flux_nkor NetCDFVariableDeep ('element2',) flux_nkor_mean NetCDFVariableAgg ('element2', 'element3') (6) We confirm that the |NetCDFVariableBase| objects received the required information: 'element2' InfoArray([[ 16., 17.], [ 18., 19.], [ 20., 21.], [ 22., 23.]]) 'element2' InfoArray([ 16.5, 18.5, 20.5, 22.5]) (7) We again prepare a |NetCDFFile| object, but now with both options `flatten` and `isolate` being enabled. To log test sequences with their original time series data does now trigger the initialisation of class |NetCDFVariableFlat|. When passing aggregated data, nothing changes: input_nied NetCDFVariableFlat ('element1', 'element2') flux_nkor NetCDFVariableFlat ('element2_0', 'element2_1') flux_nkor_mean NetCDFVariableAgg ('element2', 'element3') 'element2' InfoArray([[ 16., 17.], [ 18., 19.], [ 20., 21.], [ 22., 23.]]) 'element2' InfoArray([ 16.5, 18.5, 20.5, 22.5]) (8) We technically confirm that the `isolate` argument is passed to the constructor of subclasses of |NetCDFVariableBase| correctly: """

        aggregated = ((infoarray is not None) and
                      (infoarray.info['type'] != 'unmodified'))
        descr = sequence.descr_sequence
        if aggregated:
            descr = '_'.join([descr, infoarray.info['type']])
        if descr in self.variables:
            var_ = self.variables[descr]
        else:
            if aggregated:
                cls = NetCDFVariableAgg
            elif self._flatten:
                cls = NetCDFVariableFlat
            else:
                cls = NetCDFVariableDeep
            var_ = cls(name=descr,
                       isolate=self._isolate,
                       timeaxis=self._timeaxis)
            self.variables[descr] = var_
        var_.log(sequence, infoarray)