code: def PositionalEncoding(q_len, hidden_size, normalize=True):
      pe = torch.zeros(q_len, hidden_size)
      position = torch.arange(0, q_len).unsqueeze(1)
      div_term = torch.exp(
          torch.arange(0, hidden_size, 2)
          * -(math.log(10000.0) / hidden_size)
      )
      pe[:, 0::2] = torch.sin(position * div_term)
      pe[:, 1::2] = torch.cos(position * div_term)
      if normalize:
          pe = pe - pe.mean()
          pe = pe / (pe.std() * 10)
      return pe
  
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 2 for calling a global function with example usage: PositionalEncoding(q_len, hidden_size, normalize) and returns: pe