code: def Coord2dPosEncoding(
      q_len, hidden_size, exponential=False, normalize=True, eps=1e-3
  ):
      x = 0.5 if exponential else 1
      i = 0
      for i in range(100):
          cpe = (
              2
              * (torch.linspace(0, 1, q_len).reshape(-1, 1) ** x)
              * (torch.linspace(0, 1, hidden_size).reshape(1, -1) ** x)
              - 1
          )
          if abs(cpe.mean()) <= eps:
              break
          elif cpe.mean() > eps:
              x += 0.001
          else:
              x -= 0.001
          i += 1
      if normalize:
          cpe = cpe - cpe.mean()
          cpe = cpe / (cpe.std() * 10)
      return cpe
  
imports: import math
  import numpy
  from typing import Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from ..common._base_windows import BaseWindows
  from ..losses.pytorch import MAE
label_desc: Use image node_id 3 for calling a global function with example usage: Coord2dPosEncoding(q_len, hidden_size, exponential, normalize, eps) and returns: cpe