code: def get_mel_banks(
      num_bins: int,
      window_length_padded: int,
      sample_freq: float,
      low_freq: float,
      high_freq: float,
      vtln_low: float,
      vtln_high: float,
      vtln_warp_factor: float,
  ) -> Tuple[Tensor, Tensor]:
      """
      Returns:
          (Tensor, Tensor): The tuple consists of ``bins`` (which is
          melbank of size (``num_bins``, ``num_fft_bins``)) and ``center_freqs`` (which is
          center frequencies of bins of size (``num_bins``)).
      """
      assert num_bins > 3, "Must have at least 3 mel bins"
      assert window_length_padded % 2 == 0
      num_fft_bins = window_length_padded / 2
      nyquist = 0.5 * sample_freq
  
      if high_freq <= 0.0:
          high_freq += nyquist
  
      assert (
          (0.0 <= low_freq < nyquist)
          and (0.0 < high_freq <= nyquist)
          and (low_freq < high_freq)
      ), "Bad values in options: low-freq {} and high-freq {} vs. nyquist {}".format(
          low_freq, high_freq, nyquist
      )
  
      # fft-bin width [think of it as Nyquist-freq / half-window-length]
      fft_bin_width = sample_freq / window_length_padded
      mel_low_freq = mel_scale_scalar(low_freq)
      mel_high_freq = mel_scale_scalar(high_freq)
  
      # divide by num_bins+1 in next line because of end-effects where the bins
      # spread out to the sides.
      mel_freq_delta = (mel_high_freq - mel_low_freq) / (num_bins + 1)
  
      if vtln_high < 0.0:
          vtln_high += nyquist
  
      assert vtln_warp_factor == 1.0 or (
          (low_freq < vtln_low < high_freq)
          and (0.0 < vtln_high < high_freq)
          and (vtln_low < vtln_high)
      ), (
          "Bad values in options: vtln-low {} and vtln-high {}, versus "
          "low-freq {} and high-freq {}".format(
              vtln_low, vtln_high, low_freq, high_freq
          )
      )
  
      bin = torch.arange(num_bins).unsqueeze(1)
      left_mel = (
          mel_low_freq + bin * mel_freq_delta
      )  # size(num_bins, 1)
      center_mel = (
          mel_low_freq + (bin + 1.0) * mel_freq_delta
      )  # size(num_bins, 1)
      right_mel = (
          mel_low_freq + (bin + 2.0) * mel_freq_delta
      )  # size(num_bins, 1)
  
      if vtln_warp_factor != 1.0:
          left_mel = vtln_warp_mel_freq(
              vtln_low,
              vtln_high,
              low_freq,
              high_freq,
              vtln_warp_factor,
              left_mel,
          )
          center_mel = vtln_warp_mel_freq(
              vtln_low,
              vtln_high,
              low_freq,
              high_freq,
              vtln_warp_factor,
              center_mel,
          )
          right_mel = vtln_warp_mel_freq(
              vtln_low,
              vtln_high,
              low_freq,
              high_freq,
              vtln_warp_factor,
              right_mel,
          )
  
      center_freqs = inverse_mel_scale(center_mel)  # size (num_bins)
      # size(1, num_fft_bins)
      mel = mel_scale(
          fft_bin_width * torch.arange(num_fft_bins)
      ).unsqueeze(0)
  
      # size (num_bins, num_fft_bins)
      up_slope = (mel - left_mel) / (center_mel - left_mel)
      down_slope = (right_mel - mel) / (right_mel - center_mel)
  
      if vtln_warp_factor == 1.0:
          # left_mel < center_mel < right_mel so we can min the two slopes and clamp negative values
          bins = torch.max(
              torch.zeros(1), torch.min(up_slope, down_slope)
          )
      else:
          # warping can move the order of left_mel, center_mel, right_mel anywhere
          bins = torch.zeros_like(up_slope)
          up_idx = torch.gt(mel, left_mel) & torch.le(
              mel, center_mel
          )  # left_mel < mel <= center_mel
          down_idx = torch.gt(mel, center_mel) & torch.lt(
              mel, right_mel
          )  # center_mel < mel < right_mel
          bins[up_idx] = up_slope[up_idx]
          bins[down_idx] = down_slope[down_idx]
  
      return bins, center_freqs
  
imports: import math
  from typing import Tuple
  import torch
  import torchaudio
  from torch import Tensor
label_desc: Use image node_id 16 for calling a global function with example usage: get_mel_banks(num_bins, window_length_padded, sample_freq, low_freq, high_freq, vtln_low, vtln_high, vtln_warp_factor) and returns: bins,  center_freqs