def METHOD_NAME(self,
                  image_embeddings: paddle.Tensor,
                  image_pe: paddle.Tensor,
                  sparse_prompt_embeddings: paddle.Tensor,
                  dense_prompt_embeddings: paddle.Tensor) -> Tuple[
                      paddle.Tensor, paddle.Tensor]:
    """Predicts masks. See 'forward' for more details."""
    output_tokens = paddle.concat(
        x=[self.iou_token.weight, self.mask_tokens.weight], axis=0)
    output_tokens = output_tokens.unsqueeze(axis=0).expand(
        shape=[sparse_prompt_embeddings.shape[0], -1, -1])
    tokens = paddle.concat(
        x=(output_tokens, sparse_prompt_embeddings), axis=1)
    src = paddle.repeat_interleave(
        image_embeddings, tokens.shape[0], axis=0)
    src = src + dense_prompt_embeddings
    pos_src = paddle.repeat_interleave(image_pe, tokens.shape[0], axis=0)
    b, c, h, w = src.shape
    hs, src = self.transformer(src, pos_src, tokens)
    iou_token_out = hs[:, (0), :]
    mask_tokens_out = hs[:, 1:1 + self.num_mask_tokens, :]
    x = src
    perm_0 = list(range(x.ndim))
    perm_0[1] = 2
    perm_0[2] = 1
    """Class Method: *.view, not convert, please check whether it is torch.Tensor.*/Optimizer.*/nn.Module.*, and convert manually"""
    src = x.transpose(perm=perm_0).reshape([b, c, h, w])
    upscaled_embedding = self.output_upscaling(src)
    hyper_in_list: List[paddle.Tensor] = []
    for i in range(self.num_mask_tokens):
        hyper_in_list.append(self.output_hypernetworks_mlps[i](
            mask_tokens_out[:, (i), :]))
    hyper_in = paddle.stack(x=hyper_in_list, axis=1)
    b, c, h, w = upscaled_embedding.shape
    """Class Method: *.view, not convert, please check whether it is torch.Tensor.*/Optimizer.*/nn.Module.*, and convert manually"""
    """Class Method: *.view, not convert, please check whether it is torch.Tensor.*/Optimizer.*/nn.Module.*, and convert manually"""
    masks = (hyper_in @upscaled_embedding.reshape([b, c, h * w])).reshape(
        [b, -1, h, w])
    iou_pred = self.iou_prediction_head(iou_token_out)
    return masks, iou_pred