docstr: Predicts masks. See 'forward' for more details.
code: 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
  
file_path: PaddleSeg/contrib/SegmentAnything/segment_anything/modeling/mask_decoder.py
imports: import paddle
  from typing import List, Tuple, Type
  from common import LayerNorm2d
label_desc: Use image node_id 3 for calling the MaskDecoder obj's underlying member method code with example usage: obj.predict_masks(image_embeddings, image_pe, sparse_prompt_embeddings, dense_prompt_embeddings) and returns: masks,  iou_pred