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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 _reduce(self, func, axis=0):
 """ Reduce an array along an axis. Applies an associative/commutative function of two arguments cumulatively to all arrays along an axis. Array will be aligned so that the desired set of axes are in the keys, which may require a transpose/reshape. Parameters func : function Function of two arrays that returns a single array axis : tuple or int, optional, default=(0,) Axis or multiple axes to reduce along. """

        if self.mode == 'local':
            axes = sorted(tupleize(axis))

            # if the function is a ufunc, it can automatically handle reducing over multiple axes
            if isinstance(func, ufunc):
                inshape(self.shape, axes)
                reduced = func.reduce(self, axis=tuple(axes))
            else:
                reshaped = self._align(axes)
                reduced = reduce(func, reshaped)

            # ensure that the shape of the reduced array is valid
            expected_shape = [self.shape[i] for i in range(len(self.shape)) if i not in axes]
            if reduced.shape != tuple(expected_shape):
                raise ValueError("reduce did not yield an array with valid dimensions")

            return self._constructor(reduced[newaxis, :]).__finalize__(self)

        if self.mode == 'spark':
            reduced = self.values.reduce(func, axis, keepdims=True)
            return self._constructor(reduced).__finalize__(self)