code: def gaussian(window_size: int, sigma: float) -> torch.Tensor:
      """Gaussian Noise.
  
      Args:
          window_size (int): Window size.
          sigma (float): Noise sigma.
  
      Returns:
          torch.Tensor: Noise.
  
      """
      gauss = torch.Tensor(
          [
              exp(
                  -((x - window_size // 2) ** 2) / float(2 * sigma**2)
              )
              for x in range(window_size)
          ]
      )
      return gauss / gauss.sum()
  
file_path: espnet/espnet2/tts/prodiff/loss.py
imports: from math import exp
  from typing import Tuple
  import torch
  from torch.nn import functional
  from typeguard import check_argument_types
  from espnet.nets.pytorch_backend.fastspeech.duration_predictor import DurationPredictorLoss
  from espnet.nets.pytorch_backend.nets_utils import make_non_pad_mask
label_desc: Use image node_id 1 for calling a global function with example usage: gaussian(window_size, sigma) and returns: unknown