def predict_masks(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
Predicts masks. See 'forward' for more details.
contrib/SegmentAnything/segment_anything/modeling/mask_decoder.py