code: def _get_freq_ticks(sample_rate, offset, f_max):
      # Given the original sample rate used for generating the sweep,
      # find the x-axis value where the log-scale major frequency values fall in
      times, freq = [], []
      for exp in range(2, 5):
          for v in range(1, 10):
              f = v * 10**exp
              if f < sample_rate // 2:
                  t = (
                      _get_inverse_log_freq(f, sample_rate, offset)
                      / sample_rate
                  )
                  times.append(t)
                  freq.append(f)
      t_max = (
          _get_inverse_log_freq(f_max, sample_rate, offset)
          / sample_rate
      )
      times.append(t_max)
      freq.append(f_max)
      return times, freq
  
imports: import torch
  import torchaudio
  import torchaudio.functional
  import torchaudio.transforms
  import math
  import timeit
  import librosa
  import matplotlib.colors
  import matplotlib.pyplot
  import pandas
  import resampy
  from IPython.display import Audio
label_desc: Use image node_id 3 for calling a global function with example usage: _get_freq_ticks(sample_rate, offset, f_max) and returns: times,  freq