human
Solve the following python programming problem given the following code:
@ staticmethod 
def numpy_max_pool_2d_stride_padding(x, ds, ignore_border = True, st = None, padding = (0, 0), mode = 'max') : 
    pad_h = padding [0] 
    pad_w = padding [1] 
    h = x.shape [(- 2)] 
    w = x.shape [(- 1)] 
    assert (ds [0] > pad_h) 
    assert (ds [1] > pad_w) 
    def pad_img(x) : 
        y = numpy.zeros ((x.shape [0], x.shape [1], (x.shape [2] + (pad_h * 2)), (x.shape [3] + (pad_w * 2))), dtype = x.dtype) 
        y [:, :, pad_h : (x.shape [2] + pad_h), pad_w : (x.shape [3] + pad_w)] = x 
        return y 
img_rows = (h + (2 * pad_h)) 
    img_cols = (w + (2 * pad_w)) 
    out_r = (((img_rows - ds [0]) // st [0]) + 1) 
    out_c = (((img_cols - ds [1]) // st [1]) + 1) 
    out_shp = list (x.shape [: (- 2)]) 
    out_shp.append (out_r) 
    out_shp.append (out_c) 
    (ds0, ds1) = ds 
    (st0, st1) = st 
    output_val = numpy.zeros (out_shp) 
    y = pad_img (x) 
    func = numpy.max 
    if (mode == 'sum') : 
        func = numpy.sum 
else : 
        if (mode != 'max') : 
            func = numpy.average 
inc_pad = (mode == 'average_inc_pad') 
    for k in numpy.ndindex (* x.shape [: (- 2)]) : 
        for i in range (output_val.shape [(- 2)]) : 
            ii_st = (i * st [0]) 
            ii_end = builtins.min ((ii_st + ds [0]), img_rows) 
            if (not inc_pad) : 
                ii_st = builtins.max (ii_st, pad_h) 
                ii_end = builtins.min (ii_end, (h + pad_h)) 
for j in range (output_val.shape [(- 1)]) : 
                jj_st = (j * st [1]) 
                jj_end = builtins.min ((jj_st + ds [1]), img_cols) 
                if (not inc_pad) : 
                    jj_st = builtins.max (jj_st, pad_w) 
                    jj_end = builtins.min (jj_end, (padding + pad_w)) 
patch = y [k] [ii_st : ii_end, jj_st : jj_end] 
                output_val [k] [(i, j)] = func (patch) 
return output_val 

    
    

    Fix the buggy line: jj_end = builtins.min (jj_end, (padding + pad_w))
assistant
#Here is Python code to fix the buggy line: jj_end = builtins.

jj_end = builtins.min (jj_end, (w + pad_w))