code: def positional_encoding(
      d_model: int, length: int, device: torch.device
  ) -> torch.Tensor:
      pe = torch.zeros(length, d_model, device=device)
      position = torch.arange(
          0, length, dtype=torch.float, device=device
      ).unsqueeze(1)
      div_term = torch.exp(
          torch.arange(0, d_model, 2, device=device).float()
          * -(math.log(10000.0) / d_model)
      )
      pe[:, 0::2] = torch.sin(position * div_term)
      pe[:, 1::2] = torch.cos(position * div_term)
      pe = pe.unsqueeze(0)
      return pe
  
imports: import math
  from typing import Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from TTS.tts.layers.delightful_tts.conv_layers import ConvNorm
label_desc: Use image node_id 2 for calling a global function with example usage: positional_encoding(d_model, length, device) and returns: pe