def METHOD_NAME(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1,
                     pad_y0, pad_y1):
    _, channel, in_h, in_w = input.shape
    input = input.reshape(-1, in_h, in_w, 1)
    _, in_h, in_w, minor = input.shape
    kernel_h, kernel_w = kernel.shape
    out = input.view(-1, in_h, 1, in_w, 1, minor)
    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
    out = F.pad(
        out,
        [0, 0,
         max(pad_x0, 0),
         max(pad_x1, 0),
         max(pad_y0, 0),
         max(pad_y1, 0)])
    out = out[:,
              max(-pad_y0, 0):out.shape[1] - max(-pad_y1, 0),
              max(-pad_x0, 0):out.shape[2] - max(-pad_x1, 0)]
    out = out.permute(0, 3, 1, 2)
    out = out.reshape(
        [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
    out = F.conv2d(out, w)
    out = out.reshape(
        -1,
        minor,
        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
    )
    out = out.permute(0, 2, 3, 1)
    out = out[:, ::down_y, ::down_x, :]
    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h + down_y) // down_y
    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w + down_x) // down_x
    return out.view(-1, channel, out_h, out_w)