docstr: Returns the positional encoding used to encode point prompts, applied to a dense set of points the shape
  of the image encoding.
  
  Returns:
      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",":","Positional","encoding","with","shape","1x","(","embed_dim",")","x","(","embedding_h",")","x","(","embedding_w",")"]
code: def get_dense_pe(self) -> Tensor:
      """Returns the positional encoding used to encode point prompts, applied to a dense set of points the shape
      of the image encoding.
  
      Returns:
          Positional encoding with shape 1x(embed_dim)x(embedding_h)x(embedding_w)
      """
      return self.pe_layer(self.image_embedding_size)[None, ...]
  
imports: from __future__ import annotations
  from math import pi
  from typing import Optional
  import torch
  from torch import nn
  from kornia.contrib.models.common import LayerNorm2d
  from kornia.core import Device, Module, Tensor, concatenate, cos, sin, stack, zeros
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