code: def init_kernels(
      win_len, win_inc, fft_len, win_type=None, invers=False
  ):
      if win_type == "None" or win_type is None:
          window = np.ones(win_len)
      else:
          window = get_window(win_type, win_len, fftbins=True)  # **0.5
  
      N = fft_len
      fourier_basis = np.fft.rfft(np.eye(N))[:win_len]
      real_kernel = np.real(fourier_basis)
      imag_kernel = np.imag(fourier_basis)
      kernel = np.concatenate([real_kernel, imag_kernel], 1).T
  
      if invers:
          kernel = np.linalg.pinv(kernel).T
  
      kernel = kernel * window
      kernel = kernel[:, None, :]
      return torch.from_numpy(
          kernel.astype(np.float32)
      ), torch.from_numpy(window[None, :, None].astype(np.float32))
  
imports: import math
  import librosa
  import numpy
  import torch
  import torch.fft
  import torch.nn
  from scipy.signal import get_window
label_desc: Use image node_id 15 for calling a global function with example usage: init_kernels(win_len, win_inc, fft_len, win_type, invers) and returns: torch,  torch