def sum_blocks(array, factors):
    """Sums the elements in blocks of integer factors and pads the original
    array with zeroes if the dimensions are not perfectly divisible by factors.

    This function is different from resize and rescale in transform._warps in
    the sense that they use interpolation to upsample or downsample on a 2D
    array, while this function performs only dawnsampling but on any
    n-dimensional array and returns the sum of elements in a block of size
    factors in the original array.

    Parameters
    ----------
    array : ndarray
        Input n-dimensional array.
    factors: tuple
        Tuple containing integer values representing block length along each
        axis.

    Returns
    -------
    array : ndarray
        Downsampled array with same number of dimensions as that of input
        array.

    Example
    -------
    >>> a = np.arange(15).reshape(3, 5)
    >>> a
    array([[ 0,  1,  2,  3,  4],
           [ 5,  6,  7,  8,  9],
           [10, 11, 12, 13, 14]])
    >>> sum_blocks(a, (2,3))
    array([[21, 24],
           [33, 27]])

    """
    from ..transform._warps import _downsample
    return _downsample(array, factors)