code: def torch_window_sumsquare(w, n_frames, stride, n_fft, power=2):
      w_stacks = w.unsqueeze(-1).repeat((1, n_frames)).unsqueeze(0)
      # Window length + stride*(frames-1)
      output_len = w_stacks.shape[1] + stride * (w_stacks.shape[2] - 1)
      return fold(
          w_stacks**power,
          (1, output_len),
          kernel_size=(1, n_fft),
          stride=stride,
      )
  
imports: import sys
  import torch
  from torch.nn.functional import conv1d, fold
  import numpy
  from time import time
  import math
  from scipy.signal import get_window
  from scipy import signal
  from scipy.fftpack import fft
  import warnings
  from nnAudio.librosa_functions import *
label_desc: Use image node_id 2 for calling a global function with example usage: torch_window_sumsquare(w, n_frames, stride, n_fft, power) and returns: fold