code: from scipy.signal import spectrogram
  import numpy as np
  import matplotlib.pyplot as plt
  import torch
  import torchaudio
  
  from wettbewerb import load_references
  
  if __name__ == '__main__':
      ecg_leads = load_references("../data/training/")[0]
      for ecg_lead_ in ecg_leads:
          if ecg_lead_.shape[0] == 18000:
              ecg_lead = torch.from_numpy(ecg_lead_).float()
              break
      print(ecg_lead.shape)
      s = torchaudio.transforms.Spectrogram(n_fft=64, win_length=64, hop_length=32, power=2, normalized=False)(ecg_lead)
      s = torch.log(s.clamp(min=1e-08))
      print(s.shape)
      plt.imshow(s, aspect="auto")
      plt.show()
      print(s.shape)
  
apis: matplotlib.pyplot.imshow matplotlib.pyplot.show torch.from_numpy