code: def differenceFunction(x, N, tau_max):
      """
      Compute difference function of data x. This corresponds to equation (6) in [1]
      This solution is implemented directly with torch rfft.
  
  
      :param x: audio data (Tensor)
      :param N: length of data
      :param tau_max: integration window size
      :return: difference function
      :rtype: list
      """
  
      # x = np.array(x, np.float64) #[B,T]
      assert x.dim() == 2
      b, w = x.shape
      if w < tau_max:
          x = F.pad(
              x,
              (tau_max - w - (tau_max - w) // 2, (tau_max - w) // 2),
              "constant",
              mode="reflect",
          )
      w = tau_max
      # x_cumsum = np.concatenate((np.array([0.]), (x * x).cumsum()))
      x_cumsum = torch.cat(
          [torch.zeros([b, 1], device=x.device), (x * x).cumsum(dim=1)],
          dim=1,
      )
      size = w + tau_max
      p2 = (size // 32).bit_length()
      # p2 = ceil(log2(size+1 // 32))
      nice_numbers = (16, 18, 20, 24, 25, 27, 30, 32)
      size_pad = min(
          n * 2**p2 for n in nice_numbers if n * 2**p2 >= size
      )
      fc = torch.fft.rfft(x, size_pad)  # [B,F]
      conv = torch.fft.irfft(fc * fc.conj())[:, :tau_max]
      return (
          x_cumsum[:, w : w - tau_max : -1]
          + x_cumsum[:, w]
          - x_cumsum[:, :tau_max]
          - 2 * conv
      )
  
imports: import numpy
  import torch
  import torch.nn.functional
label_desc: Use image node_id 1 for calling a global function with example usage: differenceFunction(x, N, tau_max) and returns: unknown