class_docstr: Position embedding with learnable embedding weights.
  
  Args:
      num_feats (int): The feature dimension for each position
          along x-axis or y-axis. The final returned dimension for
          each position is 2 times of this value.
      row_num_embed (int, optional): The dictionary size of row embeddings.
          Default 50.
      col_num_embed (int, optional): The dictionary size of col embeddings.
          Default 50.
docstr: Initialize the learnable weights.
docstr_tok: ["Initialize","the","learnable","weights","."]
code: def init_weights(self):
      """Initialize the learnable weights."""
      uniform_init(self.row_embed)
      uniform_init(self.col_embed)
  
file_path: insightface/detection/scrfd/mmdet/models/utils/positional_encoding.py
imports: import math
  import torch
  import torch.nn
  from mmcv.cnn import uniform_init
  from .builder import POSITIONAL_ENCODING
label_desc: Use image node_id 2 for calling the LearnedPositionalEncoding obj's underlying member method code with example usage: obj.init_weights() without return types