code: def _bark_to_hz(
      barks: torch.Tensor, bark_scale: str = "traunmuller"
  ) -> torch.Tensor:
      """Convert bark bin numbers to frequencies.
  
      Args:
          barks (torch.Tensor): Bark frequencies
          bark_scale (str, optional): Scale to use: ``traunmuller``,``schroeder`` or ``wang``. (Default: ``traunmuller``)
  
      Returns:
          freqs (torch.Tensor): Barks converted in Hz
      """
  
      if bark_scale not in ["schroeder", "traunmuller", "wang"]:
          raise ValueError(
              'bark_scale should be one of "traunmuller", "schroeder" or "wang".'
          )
  
      if bark_scale == "wang":
          return 600.0 * torch.sinh(barks / 6.0)
      elif bark_scale == "schroeder":
          return 650.0 * torch.sinh(barks / 7.0)
      # Bark value correction
      if any(barks < 2):
          idx = barks < 2
          barks[idx] = (barks[idx] - 0.3) / 0.85
      elif any(barks > 20.1):
          idx = barks > 20.1
          barks[idx] = (barks[idx] + 4.422) / 1.22
  
      # Traunmuller Bark scale
      freqs = 1960 * ((barks + 0.53) / (26.28 - barks))
  
      return freqs
  
file_path: audio/src/torchaudio/prototype/functional/functional.py
imports: import math
  import warnings
  from typing import Optional
  import torch
  from torchaudio.functional.functional import _create_triangular_filterbank
label_desc: Use image node_id 2 for calling a global function with example usage: _bark_to_hz(barks, bark_scale) and returns: freqs, unknown, unknown