code: def rfft(x, fft_length=None):
      x = convert_to_tensor(x)
      complex_output = torch.fft.rfft(
          x, n=fft_length, dim=-1, norm="backward"
      )
      return complex_output.real, complex_output.imag
  
imports: import math
  import torch
  from keras.backend import standardize_dtype
  from keras.backend.torch.core import convert_to_tensor
  from keras.backend.torch.core import get_device
  from keras.backend.torch.numpy import pad
label_desc: Use image node_id 12 for calling a global function with example usage: rfft(x, fft_length) and returns: complex_output,  complex_output