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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 aggregate(self, dimensions=None, function=None, spreadfn=None, **kwargs):
 """Aggregates data on the supplied dimensions. Aggregates over the supplied key dimensions with the defined function. Args: dimensions: Dimension(s) to aggregate on Default to all key dimensions function: Aggregation function to apply, e.g. numpy.mean spreadfn: Secondary reduction to compute value spread Useful for computing a confidence interval, spread, or standard deviation. **kwargs: Keyword arguments passed to the aggregation function Returns: Returns the aggregated Dataset """

        if function is None:
            raise ValueError("The aggregate method requires a function to be specified")
        if dimensions is None: dimensions = self.kdims
        elif not isinstance(dimensions, list): dimensions = [dimensions]
        kdims = [self.get_dimension(d, strict=True) for d in dimensions]
        if not len(self):
            if spreadfn:
                spread_name = spreadfn.__name__
                vdims = [d for vd in self.vdims for d in [vd, vd('_'.join([vd.name, spread_name]))]]
            else:
                vdims = self.vdims
            return self.clone([], kdims=kdims, vdims=vdims)

        vdims = self.vdims
        aggregated, dropped = self.interface.aggregate(self, kdims, function, **kwargs)
        aggregated = self.interface.unpack_scalar(self, aggregated)
        vdims = [vd for vd in vdims if vd not in dropped]

        ndims = len(dimensions)
        min_d, max_d = self.params('kdims').bounds
        generic_type = (min_d is not None and ndims < min_d) or (max_d is not None and ndims > max_d)

        if spreadfn:
            error, _ = self.interface.aggregate(self, dimensions, spreadfn)
            spread_name = spreadfn.__name__
            ndims = len(vdims)
            error = self.clone(error, kdims=kdims, new_type=Dataset)
            combined = self.clone(aggregated, kdims=kdims, new_type=Dataset)
            for i, d in enumerate(vdims):
                dim = d('_'.join([d.name, spread_name]))
                dvals = error.dimension_values(d, flat=False)
                combined = combined.add_dimension(dim, ndims+i, dvals, True)
            return combined.clone(new_type=Dataset if generic_type else type(self))

        if np.isscalar(aggregated):
            return aggregated
        else:
            try:
                # Should be checking the dimensions declared on the element are compatible
                return self.clone(aggregated, kdims=kdims, vdims=vdims)
            except:
                datatype = self.params('datatype').default
                return self.clone(aggregated, kdims=kdims, vdims=vdims,
                                  new_type=Dataset if generic_type else None,
                                  datatype=datatype)