class_docstr: Mel frequency scale.
class_docstr_tok: ["Mel","frequency","scale","."]
docstr: Obtain initialization values for the mel scale.
  
  Args:
      channels: Number of channels.
      fs: Sample rate.
  
  Returns:
      torch.Tensor: Filter start frequencíes.
      torch.Tensor: Filter stop frequencies.
docstr_tok: ["Obtain","initialization","values","for","the","mel","scale",".","Args",":","channels",":","Number","of","channels",".","fs",":","Sample","rate",".","Returns",":","torch.Tensor",":","Filter","start","frequenc\u00edes",".","torch.Tensor",":","Filter","stop","frequencies","."]
code: def bank(cls, channels: int, fs: float) -> torch.Tensor:
      """Obtain initialization values for the mel scale.
  
      Args:
          channels: Number of channels.
          fs: Sample rate.
  
      Returns:
          torch.Tensor: Filter start frequencíes.
          torch.Tensor: Filter stop frequencies.
      """
      assert check_argument_types()
      # min and max bandpass edge frequencies
      min_frequency = torch.tensor(30.0)
      max_frequency = torch.tensor(fs * 0.5)
      frequencies = torch.linspace(
          cls.convert(min_frequency),
          cls.convert(max_frequency),
          channels + 2,
      )
      frequencies = cls.invert(frequencies)
      f1, f2 = frequencies[:-2], frequencies[2:]
      return torch.stack([f1, f2], dim=1)
  
imports: import math
  from typing import Union
  import torch
  from typeguard import check_argument_types
label_desc: Use image node_id 3 for calling the MelScale obj's underlying member method code with example usage: obj.bank(cls, channels, fs) and returns: torch