docstr: Returns the positional encoding used to encode point prompts,
  applied to a dense set of points the shape of the image encoding.
  
  Returns:
    torch.Tensor: Positional encoding with shape
      1x(embed_dim)x(embedding_h)x(embedding_w)
docstr_tok: ["Returns","the","positional","encoding","used","to","encode","point","prompts",",","applied","to","a","dense","set","of","points","the","shape","of","the","image","encoding",".","Returns",":","torch.Tensor",":","Positional","encoding","with","shape","1x","(","embed_dim",")","x","(","embedding_h",")","x","(","embedding_w",")"]
code: def get_dense_pe(self) -> paddle.Tensor:
      """
      Returns the positional encoding used to encode point prompts,
      applied to a dense set of points the shape of the image encoding.
  
      Returns:
        torch.Tensor: Positional encoding with shape
          1x(embed_dim)x(embedding_h)x(embedding_w)
      """
      return self.pe_layer(self.image_embedding_size).unsqueeze(axis=0)
  
file_path: PaddleSeg/contrib/SegmentAnything/segment_anything/modeling/prompt_encoder.py
imports: import paddle
  import numpy
  from typing import Any, Optional, Tuple, Type
  from common import LayerNorm2d
label_desc: Use image node_id 2 for calling the PromptEncoder obj's underlying member method code with example usage: obj.get_dense_pe() and returns: self