code: def create_fb_matrix(
      n_freqs: int,
      f_min: float,
      f_max: float,
      n_mels: int,
      sample_rate: int,
      norm: Optional[str] = None,
  ) -> torch.Tensor:
      """Create a frequency bin conversion matrix.
      Args:
          n_freqs (int): Number of frequencies to highlight/apply
          f_min (float): Minimum frequency (Hz)
          f_max (float): Maximum frequency (Hz)
          n_mels (int): Number of mel filterbanks
          sample_rate (int): Sample rate of the audio waveform
          norm (Optional[str]): If 'slaney',
          divide the triangular mel weights by the width of the mel band
          (area normalization). (Default: None)
      Returns:
          Tensor: Triangular filter banks (fb matrix) of size (n_freqs, n_mels)
          meaning number of frequencies to highlight/apply to x the number of filterbanks.
          Each column is a filterbank so that assuming there is a matrix A of
          size (..., n_freqs), the applied result would be
          A * create_fb_matrix(A.size(-1), ...).
      """
  
      if norm is not None and norm != "slaney":
          raise ValueError("norm must be one of None or 'slaney'")
  
      # freq bins
      # Equivalent filterbank construction by Librosa
      all_freqs = torch.linspace(0, sample_rate // 2, n_freqs)
  
      # calculate mel freq bins
      # hertz to mel(f) is 2595. * math.log10(1. + (f / 700.))
      m_min = 2595.0 * math.log10(1.0 + (f_min / 700.0))
      m_max = 2595.0 * math.log10(1.0 + (f_max / 700.0))
      m_pts = torch.linspace(m_min, m_max, n_mels + 2)
      # mel to hertz(mel) is 700. * (10**(mel / 2595.) - 1.)
      f_pts = 700.0 * (10 ** (m_pts / 2595.0) - 1.0)
      # calculate the difference between each mel point and each stft freq point in hertz
      f_diff = f_pts[1:] - f_pts[:-1]  # (n_mels + 1)
      slopes = f_pts.unsqueeze(0) - all_freqs.unsqueeze(
          1
      )  # (n_freqs, n_mels + 2)
      # create overlapping triangles
      down_slopes = (-1.0 * slopes[:, :-2]) / f_diff[
          :-1
      ]  # (n_freqs, n_mels)
      up_slopes = slopes[:, 2:] / f_diff[1:]  # (n_freqs, n_mels)
      fb = torch.min(down_slopes, up_slopes)
      fb = torch.clamp(fb, 1e-6, 1)
  
      if norm is not None and norm == "slaney":
          # Slaney-style mel is scaled to be approx constant energy per channel
          enorm = 2.0 / (f_pts[2 : n_mels + 2] - f_pts[:n_mels])
          fb *= enorm.unsqueeze(0)
      return fb
  
imports: import logging
  import math
  from typing import Any, Dict, List, Optional
  import numpy
  import torch
  import torch.nn.functional
  from espnet2.gan_svs.visinger2.ddsp import remove_above_nyquist, scale_function, upsample
  from espnet2.gan_tts.hifigan import HiFiGANMultiPeriodDiscriminator, HiFiGANMultiScaleDiscriminator, HiFiGANMultiScaleMultiPeriodDiscriminator, HiFiGANPeriodDiscriminator, HiFiGANScaleDiscriminator
  from espnet2.gan_tts.hifigan.residual_block import ResidualBlock
label_desc: Use image node_id 1 for calling a global function with example usage: create_fb_matrix(n_freqs, f_min, f_max, n_mels, sample_rate, norm) and returns: fb