code: def positional_encoding(pe, learn_pe, q_len, hidden_size):
      # Positional encoding
      if pe == None:
          W_pos = torch.empty(
              (q_len, hidden_size)
          )  # pe = None and learn_pe = False can be used to measure impact of pe
          nn.init.uniform_(W_pos, -0.02, 0.02)
          learn_pe = False
      elif pe == "zero":
          W_pos = torch.empty((q_len, 1))
          nn.init.uniform_(W_pos, -0.02, 0.02)
      elif pe == "zeros":
          W_pos = torch.empty((q_len, hidden_size))
          nn.init.uniform_(W_pos, -0.02, 0.02)
      elif pe == "normal" or pe == "gauss":
          W_pos = torch.zeros((q_len, 1))
          torch.nn.init.normal_(W_pos, mean=0.0, std=0.1)
      elif pe == "uniform":
          W_pos = torch.zeros((q_len, 1))
          nn.init.uniform_(W_pos, a=0.0, b=0.1)
      elif pe == "lin1d":
          W_pos = Coord1dPosEncoding(
              q_len, exponential=False, normalize=True
          )
      elif pe == "exp1d":
          W_pos = Coord1dPosEncoding(
              q_len, exponential=True, normalize=True
          )
      elif pe == "lin2d":
          W_pos = Coord2dPosEncoding(
              q_len, hidden_size, exponential=False, normalize=True
          )
      elif pe == "exp2d":
          W_pos = Coord2dPosEncoding(
              q_len, hidden_size, exponential=True, normalize=True
          )
      elif pe == "sincos":
          W_pos = PositionalEncoding(q_len, hidden_size, normalize=True)
      else:
          raise ValueError(
              f"{pe} is not a valid pe (positional encoder. Available types: 'gauss'=='normal', \
          'zeros', 'zero', uniform', 'lin1d', 'exp1d', 'lin2d', 'exp2d', 'sincos', None.)"
          )
      return nn.Parameter(W_pos, requires_grad=learn_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 5 for calling a global function with example usage: positional_encoding(pe, learn_pe, q_len, hidden_size) and returns: nn