def METHOD_NAME( a, resize = 1.5, angle = 20., interpolation = "linear", blocks = (16,16,1)  ):
    """
    Rotates the array. The new array has the new size and centers the
    picture in the middle.
    a             - array (2-dim)
    resize        - new_image w/old_image w
    angle         - degrees to rotate the image
    interpolation - "linear" or None
    blocks        - given to the kernel when run
    returns: a new array with dtype=uint8 containing the rotated image
    """
    angle = angle/180. *pi
    # Convert this image to float. Unsigned int texture gave 
    # strange results for me. This conversion is slow though :(
    a = a.astype("float32")
    # Calculate the dimensions of the new image
    calc_x = lambda x_y: (x_y[0]*a.shape[1]/2.*cos(angle)-x_y[1]*a.shape[0]/2.*sin(angle))
    calc_y = lambda x_y1: (x_y1[0]*a.shape[1]/2.*sin(angle)+x_y1[1]*a.shape[0]/2.*cos(angle))
    xs = [ calc_x(p) for p in [ (-1.,-1.),(1.,-1.),(1.,1.),(-1.,1.) ] ]
    ys = [ calc_y(p) for p in [ (-1.,-1.),(1.,-1.),(1.,1.),(-1.,1.) ] ]
    new_image_dim = (
        int(numpy.ceil(max(ys)-min(ys))*resize),
        int(numpy.ceil(max(xs)-min(xs))*resize),
    )
    # Now generate the cuda texture
    cuda.matrix_to_texref(a, texref, order="C")
    # We could set the next if we wanted to address the image
    # in normalized coordinates ( 0 <= coordinate < 1.)
    # texref.set_flags(cuda.TRSF_NORMALIZED_COORDINATES)
    if interpolation == "linear":
        texref.set_filter_mode(cuda.filter_mode.LINEAR)
    # Calculate the gridsize. This is entirely given by the size of our image. 
    gridx = new_image_dim[0]/blocks[0] if \
            new_image_dim[0]%blocks[0]==1 else new_image_dim[0]/blocks[0] +1
    gridy = new_image_dim[1]/blocks[1] if \
            new_image_dim[1]%blocks[1]==0 else new_image_dim[1]/blocks[1] +1
    # Get the output image
    output = numpy.zeros(new_image_dim,dtype="uint8")
    # Call the kernel
    copy_texture_func(
        numpy.float32(resize), numpy.float32(angle),
        numpy.uint16(a.shape[1]), numpy.uint16(a.shape[0]),
        numpy.uint16(new_image_dim[1]), numpy.uint16(new_image_dim[0]),
            cuda.Out(output),texrefs=[texref],block=blocks,grid=(gridx,gridy))
    return output