code: def convert_parameter_to_frequencies(f1, f2, sample_rate, sorted):
      """Convert parameters to frequencies.
  
      Parameters f1 and f2 denote frequencies normed to the sampling frequency.
  
      Args:
          f1: Lower frequency relative to sample rate.
          f2: Higher frequency  relative to sample rate.
          sample_rate: Sample rate.
          sorted: Sort filters by their center frequency.
  
      Returns:
          f_mins: Absolute lower frequency.
          f_maxs: Absolute higher frequency.
          f_mins: Absolute center frequency.
      """
      f_mins = np.abs(f1) * sample_rate
      f_maxs = (np.abs(f1) + np.abs(f2 - f1)) * sample_rate
      f_mins = np.clip(f_mins, 0, sample_rate / 2)
      f_maxs = np.clip(f_maxs, 0, sample_rate / 2)
      f_mids = (f_maxs + f_mins) / 2
      if sorted:
          order = np.argsort(f_mids)
          f_mins, f_mids, f_maxs = (
              f_mins[order],
              f_mids[order],
              f_maxs[order],
          )
      return f_mins, f_maxs, f_mids
  
file_path: espnet/egs2/dns_icassp22/enh1/pyscripts/utils/plot_sinc_filters.py
imports: import argparse
  import sys
  from pathlib import Path
  import matplotlib.pyplot
  import numpy
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: convert_parameter_to_frequencies(f1, f2, sample_rate, sorted) and returns: f_mins,  f_maxs,  f_mids